diff --git a/pina/geometry/simplex.py b/pina/geometry/simplex.py
index 0153540..ac1e9c0 100644
--- a/pina/geometry/simplex.py
+++ b/pina/geometry/simplex.py
@@ -48,7 +48,12 @@ class SimplexDomain(Location):
         matrix_labels = simplex_matrix[0].labels
         if not all(vertex.labels == matrix_labels for vertex in simplex_matrix):
             raise ValueError(f"Labels don't match.")
-
+        
+        # check consistency dimensions
+        dim_simplex = len(matrix_labels)
+        if len(simplex_matrix) != dim_simplex + 1:
+            raise ValueError("An n-dimensional simplex is composed by n + 1 tensors of dimension n.")
+        
         # creating vertices matrix
         self._vertices_matrix = torch.cat(simplex_matrix)
         self._vertices_matrix.labels = matrix_labels
diff --git a/tests/test_geometry/test_simplex.py b/tests/test_geometry/test_simplex.py
index 3eacd36..8c4d864 100644
--- a/tests/test_geometry/test_simplex.py
+++ b/tests/test_geometry/test_simplex.py
@@ -22,6 +22,7 @@ def test_constructor():
         sample_surface=True,
     )
     with pytest.raises(ValueError):
+        # different labels
         SimplexDomain(
             [
                 LabelTensor(torch.tensor([[0, 0]]), labels=["x", "y"]),
@@ -29,6 +30,7 @@ def test_constructor():
                 LabelTensor(torch.tensor([[0, 2]]), labels=["x", "a"]),
             ]
         )
+        # not LabelTensor
         SimplexDomain(
             [
                 LabelTensor(torch.tensor([[0, 0]]), labels=["x", "y"]),
@@ -36,6 +38,15 @@ def test_constructor():
                 LabelTensor(torch.tensor([[0, 2]]), labels=["x", "y"]),
             ]
         )
+        #  different number of vertices
+        SimplexDomain(
+                    [
+                        LabelTensor(torch.tensor([[ 0., -2.]]), labels=["x", "y"]),
+                        LabelTensor(torch.tensor([[-.5, -.5]]), labels=["x", "y"]),
+                        LabelTensor(torch.tensor([[-2.,  0.]]), labels=["x", "y"]),
+                        LabelTensor(torch.tensor([[-.5,  .5]]), labels=["x", "y"]),
+                    ]
+                )
 
 def test_sample():
     # sampling inside