diff --git a/CHANGELOG.md b/CHANGELOG.md
index 167bc7df4a741..ec7e545db501f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -86,6 +86,13 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Added a `MisconfigurationException` if user provided `opt_idx` in scheduler config doesn't match with actual optimizer index of its respective optimizer ([#11247](https://github.com/PyTorchLightning/pytorch-lightning/pull/11247))
 
+
+- Added a `loggers` property to `Trainer` which returns a list of loggers provided by the user ([#11683](https://github.com/PyTorchLightning/pytorch-lightning/pull/11683))
+
+
+- Added a `loggers` property to `LightningModule` which retrieves the `loggers` property from `Trainer` ([#11683](https://github.com/PyTorchLightning/pytorch-lightning/pull/11683))
+
+
 - Added support for DDP when using a `CombinedLoader` for the training data ([#11648](https://github.com/PyTorchLightning/pytorch-lightning/pull/11648))
 
 
diff --git a/docs/source/common/lightning_module.rst b/docs/source/common/lightning_module.rst
index b1436290c8c19..18a0fbead4e7d 100644
--- a/docs/source/common/lightning_module.rst
+++ b/docs/source/common/lightning_module.rst
@@ -985,6 +985,19 @@ The current logger being used (tensorboard or other supported logger)
         # the particular logger
         tensorboard_logger = self.logger.experiment
 
+loggers
+~~~~~~~
+
+The list of loggers currently being used by the Trainer.
+
+.. code-block:: python
+
+    def training_step(self, batch, batch_idx):
+        # List of LightningLoggerBase objects
+        loggers = self.loggers
+        for logger in loggers:
+            logger.log_metrics({"foo": 1.0})
+
 local_rank
 ~~~~~~~~~~~
 
diff --git a/docs/source/common/trainer.rst b/docs/source/common/trainer.rst
index 94c6a86bf393d..2126472b95169 100644
--- a/docs/source/common/trainer.rst
+++ b/docs/source/common/trainer.rst
@@ -1734,16 +1734,28 @@ The current epoch
             pass
 
 
-logger (p)
-**********
+logger
+*******
 
 The current logger being used. Here's an example using tensorboard
 
 .. code-block:: python
 
-    def training_step(self, batch, batch_idx):
-        logger = self.trainer.logger
-        tensorboard = logger.experiment
+    logger = trainer.logger
+    tensorboard = logger.experiment
+
+
+loggers
+********
+
+The list of loggers currently being used by the Trainer.
+
+.. code-block:: python
+
+    # List of LightningLoggerBase objects
+    loggers = trainer.loggers
+    for logger in loggers:
+        logger.log_metrics({"foo": 1.0})
 
 
 logged_metrics
diff --git a/pytorch_lightning/core/lightning.py b/pytorch_lightning/core/lightning.py
index bd0c04d715133..ff6693823b586 100644
--- a/pytorch_lightning/core/lightning.py
+++ b/pytorch_lightning/core/lightning.py
@@ -37,6 +37,7 @@
 from pytorch_lightning.core.mixins import DeviceDtypeModuleMixin, HyperparametersMixin
 from pytorch_lightning.core.optimizer import LightningOptimizer
 from pytorch_lightning.core.saving import ModelIO
+from pytorch_lightning.loggers import LightningLoggerBase
 from pytorch_lightning.trainer.connectors.logger_connector.fx_validator import _FxValidator
 from pytorch_lightning.utilities import _IS_WINDOWS, _TORCH_GREATER_EQUAL_1_10, GradClipAlgorithmType
 from pytorch_lightning.utilities.apply_func import apply_to_collection, convert_to_tensors
@@ -75,6 +76,7 @@ class LightningModule(
             "global_rank",
             "local_rank",
             "logger",
+            "loggers",
             "model_size",
             "automatic_optimization",
             "truncated_bptt_steps",
@@ -247,10 +249,15 @@ def truncated_bptt_steps(self, truncated_bptt_steps: int) -> None:
         self._truncated_bptt_steps = truncated_bptt_steps
 
     @property
-    def logger(self):
+    def logger(self) -> Optional[LightningLoggerBase]:
         """Reference to the logger object in the Trainer."""
         return self.trainer.logger if self.trainer else None
 
+    @property
+    def loggers(self) -> List[LightningLoggerBase]:
+        """Reference to the loggers object in the Trainer."""
+        return self.trainer.loggers if self.trainer else []
+
     def _apply_batch_transfer_handler(
         self, batch: Any, device: Optional[torch.device] = None, dataloader_idx: int = 0
     ) -> Any:
diff --git a/pytorch_lightning/trainer/trainer.py b/pytorch_lightning/trainer/trainer.py
index 91d319a113173..f73e16604dcba 100644
--- a/pytorch_lightning/trainer/trainer.py
+++ b/pytorch_lightning/trainer/trainer.py
@@ -565,7 +565,7 @@ def __init__(
         self.__init_profiler(profiler)
 
         # init logger flags
-        self.logger: Optional[LightningLoggerBase]
+        self._loggers: List[LightningLoggerBase]
         self.logger_connector.on_trainer_init(logger, flush_logs_every_n_steps, log_every_n_steps, move_metrics_to_cpu)
 
         # init debugging flags
@@ -2553,6 +2553,37 @@ def _active_loop(self) -> Optional[Union[FitLoop, EvaluationLoop, PredictionLoop
     Logging properties
     """
 
+    @property
+    def logger(self) -> Optional[LightningLoggerBase]:
+        if len(self.loggers) == 0:
+            return None
+        if len(self.loggers) == 1:
+            return self.loggers[0]
+        else:
+            rank_zero_warn(
+                "Using trainer.logger when Trainer is configured to use multiple loggers."
+                " This behavior will change in v1.8 when LoggerCollection is removed, and"
+                " trainer.logger will return the first logger in trainer.loggers"
+            )
+            return LoggerCollection(self.loggers)
+
+    @logger.setter
+    def logger(self, logger: Optional[LightningLoggerBase]) -> None:
+        if not logger:
+            self.loggers = []
+        elif isinstance(logger, LoggerCollection):
+            self.loggers = list(logger)
+        else:
+            self.loggers = [logger]
+
+    @property
+    def loggers(self) -> List[LightningLoggerBase]:
+        return self._loggers
+
+    @loggers.setter
+    def loggers(self, loggers: Optional[List[LightningLoggerBase]]) -> None:
+        self._loggers = loggers if loggers else []
+
     @property
     def callback_metrics(self) -> dict:
         return self.logger_connector.callback_metrics
diff --git a/tests/core/test_lightning_module.py b/tests/core/test_lightning_module.py
index 70abaed4492d7..bf19040e04b89 100644
--- a/tests/core/test_lightning_module.py
+++ b/tests/core/test_lightning_module.py
@@ -76,6 +76,17 @@ def test_property_logger(tmpdir):
     assert model.logger == logger
 
 
+def test_property_loggers(tmpdir):
+    """Test that loggers in LightningModule is accessible via the Trainer."""
+    model = BoringModel()
+    assert model.loggers == []
+
+    logger = TensorBoardLogger(tmpdir)
+    trainer = Trainer(logger=logger)
+    model.trainer = trainer
+    assert model.loggers == [logger]
+
+
 def test_toggle_untoggle_2_optimizers_no_shared_parameters(tmpdir):
     class TestModel(BoringModel):
         def training_step(self, batch, batch_idx, optimizer_idx=None):
diff --git a/tests/profiler/test_profiler.py b/tests/profiler/test_profiler.py
index 8d92c4318b4f2..522b56ca90e3f 100644
--- a/tests/profiler/test_profiler.py
+++ b/tests/profiler/test_profiler.py
@@ -24,7 +24,7 @@
 
 from pytorch_lightning import Callback, Trainer
 from pytorch_lightning.callbacks import EarlyStopping, StochasticWeightAveraging
-from pytorch_lightning.loggers.base import LoggerCollection
+from pytorch_lightning.loggers.base import DummyLogger, LoggerCollection
 from pytorch_lightning.loggers.tensorboard import TensorBoardLogger
 from pytorch_lightning.profiler import AdvancedProfiler, PassThroughProfiler, PyTorchProfiler, SimpleProfiler
 from pytorch_lightning.profiler.pytorch import RegisterRecordFunction, warning_cache
@@ -493,7 +493,7 @@ def look_for_trace(trace_dir):
 
     model = BoringModel()
     # Wrap the logger in a list so it becomes a LoggerCollection
-    logger = [TensorBoardLogger(save_dir=tmpdir)]
+    logger = [TensorBoardLogger(save_dir=tmpdir), DummyLogger()]
     trainer = Trainer(default_root_dir=tmpdir, profiler="pytorch", logger=logger, limit_train_batches=5, max_epochs=1)
     assert isinstance(trainer.logger, LoggerCollection)
     trainer.fit(model)
diff --git a/tests/trainer/properties/test_log_dir.py b/tests/trainer/properties/test_log_dir.py
index 277a2f105efd6..71920a6b079bf 100644
--- a/tests/trainer/properties/test_log_dir.py
+++ b/tests/trainer/properties/test_log_dir.py
@@ -16,6 +16,7 @@
 from pytorch_lightning import Trainer
 from pytorch_lightning.callbacks import ModelCheckpoint
 from pytorch_lightning.loggers import LoggerCollection, TensorBoardLogger
+from pytorch_lightning.loggers.base import DummyLogger
 from tests.helpers.boring_model import BoringModel
 
 
@@ -117,7 +118,7 @@ def test_logdir_logger_collection(tmpdir):
     trainer = Trainer(
         default_root_dir=default_root_dir,
         max_steps=2,
-        logger=[TensorBoardLogger(save_dir=save_dir, name="custom_logs")],
+        logger=[TensorBoardLogger(save_dir=save_dir, name="custom_logs"), DummyLogger()],
     )
     assert isinstance(trainer.logger, LoggerCollection)
     assert trainer.log_dir == default_root_dir
diff --git a/tests/trainer/properties/test_loggers.py b/tests/trainer/properties/test_loggers.py
new file mode 100644
index 0000000000000..606c7b641ae1d
--- /dev/null
+++ b/tests/trainer/properties/test_loggers.py
@@ -0,0 +1,91 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from pytorch_lightning import Trainer
+from pytorch_lightning.loggers import LoggerCollection, TensorBoardLogger
+from tests.loggers.test_base import CustomLogger
+
+
+def test_trainer_loggers_property():
+    """Test for correct initialization of loggers in Trainer."""
+    logger1 = CustomLogger()
+    logger2 = CustomLogger()
+
+    # trainer.loggers should be a copy of the input list
+    trainer = Trainer(logger=[logger1, logger2])
+
+    assert trainer.loggers == [logger1, logger2]
+
+    # trainer.loggers should create a list of size 1
+    trainer = Trainer(logger=logger1)
+
+    assert trainer.loggers == [logger1]
+
+    # trainer.loggers should be an empty list
+    trainer = Trainer(logger=False)
+
+    assert trainer.loggers == []
+
+    # trainer.loggers should be a list of size 1 holding the default logger
+    trainer = Trainer(logger=True)
+
+    assert trainer.loggers == [trainer.logger]
+    assert type(trainer.loggers[0]) == TensorBoardLogger
+
+
+def test_trainer_loggers_setters():
+    """Test the behavior of setters for trainer.logger and trainer.loggers."""
+    logger1 = CustomLogger()
+    logger2 = CustomLogger()
+    logger_collection = LoggerCollection([logger1, logger2])
+    logger_collection_2 = LoggerCollection([logger2])
+
+    trainer = Trainer()
+    assert type(trainer.logger) == TensorBoardLogger
+    assert trainer.loggers == [trainer.logger]
+
+    # Test setters for trainer.logger
+    trainer.logger = logger1
+    assert trainer.logger == logger1
+    assert trainer.loggers == [logger1]
+
+    trainer.logger = logger_collection
+    assert trainer.logger._logger_iterable == logger_collection._logger_iterable
+    assert trainer.loggers == [logger1, logger2]
+
+    # LoggerCollection of size 1 should result in trainer.logger becoming the contained logger.
+    trainer.logger = logger_collection_2
+    assert trainer.logger == logger2
+    assert trainer.loggers == [logger2]
+
+    trainer.logger = None
+    assert trainer.logger is None
+    assert trainer.loggers == []
+
+    # Test setters for trainer.loggers
+    trainer.loggers = [logger1, logger2]
+    assert trainer.loggers == [logger1, logger2]
+    assert trainer.logger._logger_iterable == logger_collection._logger_iterable
+
+    trainer.loggers = [logger1]
+    assert trainer.loggers == [logger1]
+    assert trainer.logger == logger1
+
+    trainer.loggers = []
+    assert trainer.loggers == []
+    assert trainer.logger is None
+
+    trainer.loggers = None
+    assert trainer.loggers == []
+    assert trainer.logger is None