diff --git a/pina/trainer.py b/pina/trainer.py
index 8831d40..f0175e9 100644
--- a/pina/trainer.py
+++ b/pina/trainer.py
@@ -22,6 +22,7 @@ class Trainer(lightning.pytorch.Trainer):
         automatic_batching=None,
         num_workers=None,
         pin_memory=None,
+        shuffle=None,
         **kwargs,
     ):
         """
@@ -34,13 +35,13 @@ class Trainer(lightning.pytorch.Trainer):
             If ``batch_size=None`` all
             samples are loaded and data are not batched, defaults to None.
         :type batch_size: int | None
-        :param train_size: percentage of elements in the train dataset
+        :param train_size: Percentage of elements in the train dataset.
         :type train_size: float
-        :param test_size: percentage of elements in the test dataset
+        :param test_size: Percentage of elements in the test dataset.
         :type test_size: float
-        :param val_size: percentage of elements in the val dataset
+        :param val_size: Percentage of elements in the val dataset.
         :type val_size: float
-        :param predict_size: percentage of elements in the predict dataset
+        :param predict_size: Percentage of elements in the predict dataset.
         :type predict_size: float
         :param compile: if True model is compiled before training,
             default False. For Windows users compilation is always disabled.
@@ -49,9 +50,13 @@ class Trainer(lightning.pytorch.Trainer):
             performed. Please avoid using automatic batching when batch_size is
             large, default False.
         :type automatic_batching: bool
-        :param num_workers: Number of worker threads for data loading. Default 0 (serial loading)
+        :param num_workers: Number of worker threads for data loading.
+            Default 0 (serial loading).
         :type num_workers: int
-        :param pin_memory: Whether to use pinned memory for faster data transfer to GPU. (Default False)
+        :param pin_memory: Whether to use pinned memory for faster data
+            transfer to GPU. Default False.
+        :type pin_memory: bool
+        :param shuffle: Whether to shuffle the data for training. Default False.
         :type pin_memory: bool
 
         :Keyword Arguments:
@@ -77,6 +82,10 @@ class Trainer(lightning.pytorch.Trainer):
             check_consistency(pin_memory, int)
         else:
             num_workers = 0
+        if shuffle is not None:
+            check_consistency(shuffle, bool)
+        else:
+            shuffle = False
         if train_size + test_size + val_size + predict_size > 1:
             raise ValueError(
                 "train_size, test_size, val_size and predict_size "
@@ -131,6 +140,7 @@ class Trainer(lightning.pytorch.Trainer):
             automatic_batching,
             pin_memory,
             num_workers,
+            shuffle,
         )
 
         # logging
@@ -166,6 +176,7 @@ class Trainer(lightning.pytorch.Trainer):
         automatic_batching,
         pin_memory,
         num_workers,
+        shuffle,
     ):
         """
         This method is used here because is resampling is needed
@@ -196,6 +207,7 @@ class Trainer(lightning.pytorch.Trainer):
             automatic_batching=automatic_batching,
             num_workers=num_workers,
             pin_memory=pin_memory,
+            shuffle=shuffle,
         )
 
     def train(self, **kwargs):