diff --git a/monai/networks/blocks/crf.py b/monai/networks/blocks/crf.py
index 736259cb96..219f1a70e2 100644
--- a/monai/networks/blocks/crf.py
+++ b/monai/networks/blocks/crf.py
@@ -43,7 +43,7 @@ def __init__(
         bilateral_color_sigma: float = 0.5,
         gaussian_spatial_sigma: float = 5.0,
         update_factor: float = 3.0,
-        compatability_matrix: Optional[torch.Tensor] = None,
+        compatibility_matrix: Optional[torch.Tensor] = None,
     ):
         """
         Args:
@@ -54,7 +54,7 @@ def __init__(
             bilateral_color_sigma: standard deviation in color space for the bilateral term.
             gaussian_spatial_sigma: standard deviation in spatial coordinates for the gaussian term.
             update_factor: determines the magnitude of each update.
-            compatability_matrix: a matrix describing class compatability, should be NxN where N is the numer of classes.
+            compatibility_matrix: a matrix describing class compatibility, should be NxN where N is the numer of classes.
         """
         super(CRF, self).__init__()
         self.iterations = iterations
@@ -64,7 +64,7 @@ def __init__(
         self.bilateral_color_sigma = bilateral_color_sigma
         self.gaussian_spatial_sigma = gaussian_spatial_sigma
         self.update_factor = update_factor
-        self.compatability_matrix = compatability_matrix
+        self.compatibility_matrix = compatibility_matrix
 
     def forward(self, input_tensor: torch.Tensor, reference_tensor: torch.Tensor):
         """
@@ -98,10 +98,10 @@ def forward(self, input_tensor: torch.Tensor, reference_tensor: torch.Tensor):
             # combining filter outputs
             combined_output = self.bilateral_weight * bliateral_output + self.gaussian_weight * gaussian_output
 
-            # optionally running a compatability transform
-            if self.compatability_matrix is not None:
+            # optionally running a compatibility transform
+            if self.compatibility_matrix is not None:
                 flat = combined_output.flatten(start_dim=2).permute(0, 2, 1)
-                flat = torch.matmul(flat, self.compatability_matrix)
+                flat = torch.matmul(flat, self.compatibility_matrix)
                 combined_output = flat.permute(0, 2, 1).reshape(combined_output.shape)
 
             # update and normalize