Haralick features, am I approaching this correctly?
Using the Euclidean distance on a feature space will eventually result in a Minimum Distance Classifier which would provide information on the existence or not of a relationship that you seem to be after (for more information please see: http://homepages.inf.ed.ac.uk/rbf/HIPR2/classify.htm). 

In your case, if you are using all Haralick features, you are looking at a 13-14 dimensional feature space and a decision boundary that is not a line any more but a plane.

The usual way to do this is to first calculate the "class centroids", that is, the mean values of each feature for each set of sand and checker board images and then, given the features of the test image, calculate its distance from the centroids of each class and select the minimum (i.e. the class centroid that the test-image features are more closely to).

Hope this helps.