We address the issue of how statistical and information-theoric measures can be employed to quantify the categorization process of a simulated robotic agent interacting with its local environment. We show how correlation, entropy, and mutual information can help identify distinct informational structure which can be used for object classification. Further, by means of the isometric feature mapping algorithm, we analyze the weights of a neural network designed to find clusters based on these distinct information theoretic characteristics of the object’s shape, size and color. We conclude that an understanding of the information-theoretic implications of categorization could help design robots with improved catego rization and better exploration strategies.