diff --git a/pina/_src/core/label_tensor.py b/pina/_src/core/label_tensor.py
index 41bccc6fc..9a81fb3a6 100644
--- a/pina/_src/core/label_tensor.py
+++ b/pina/_src/core/label_tensor.py
@@ -2,7 +2,6 @@
 
 from copy import copy, deepcopy
 import torch
-from torch import Tensor
 
 
 class LabelTensor(torch.Tensor):
@@ -25,7 +24,6 @@ def __new__(cls, x, labels, *args, **kwargs):
             class.
         :rtype: LabelTensor
         """
-
         if isinstance(x, LabelTensor):
             return x
         return super().__new__(cls, x, *args, **kwargs)
@@ -39,8 +37,7 @@ def tensor(self):
         :return: Tensor part of the :class:`~pina.label_tensor.LabelTensor`.
         :rtype: torch.Tensor
         """
-
-        return self.as_subclass(Tensor)
+        return self.as_subclass(torch.Tensor)
 
     def __init__(self, x, labels):
         """
@@ -366,25 +363,44 @@ def cat(tensors, dim=0):
         return cat_tensor
 
     @staticmethod
-    def stack(tensors):
+    def stack(tensors, dim=0):
         """
         Stacks a list of tensors along a new dimension. For more details, see
         :meth:`torch.stack`.
 
-        :param list[LabelTensor] tensors: A list of tensors to stack.
-            All tensors must have the same shape.
+        :param list[LabelTensor] tensors: The list of tensors to stack. All
+            tensors must have the same shape and labels.
+        :param int dim: The dimension along which to insert the new stacked
+            dimension. It follows torch.stack semantics. The new dimension
+            cannot be inserted in a dimension that already has labels.
         :return: A new :class:`~pina.label_tensor.LabelTensor` instance obtained
             by stacking the input tensors.
         :rtype: LabelTensor
         """
+        # Ensure all tensors are LabelTensor instances
+        if not all(isinstance(tensor, LabelTensor) for tensor in tensors):
+            raise TypeError("All tensors must be LabelTensor instances.")
 
-        # Perform stacking in torch
-        new_tensor = torch.stack(tensors)
+        # Ensure all tensors have the same labels
+        reference_labels = tensors[0].labels
+        for tensor in tensors:
+            if tensor.labels != reference_labels:
+                raise ValueError("All tensors must have the same labels.")
 
-        # Increase labels keys by 1
-        labels = tensors[0]._labels
-        labels = {key + 1: value for key, value in labels.items()}
+        # Avoid stacking along the labels dimension
+        if dim in tensors[0]._labels:
+            raise ValueError(
+                f"Cannot stack along labels dimension {dim}. "
+                "Choose a lower dimension index."
+            )
+
+        # Stack tensors
+        new_tensor = torch.stack(tensors, dim=dim)
+
+        # Reassign labels to the new tensor
+        labels = {key + 1: value for key, value in tensors[0]._labels.items()}
         new_tensor._labels = labels
+
         return new_tensor
 
     def requires_grad_(self, mode=True):