In nature, the generalization in object recognition is based not in the stimuli space but the internal representation as Shepard proposed in his Universal Law of Generalization. The fact that this law applies to many biological system gives it its universal character. The extension of this universality to artificial systems remains to be studied. In this paper, we present how an artificial agent generates the internal representation of a collection of stimuli from a previous experience. More-over, from the internal representation and the classification of the artificial agent, we are already able to verify Shepard’s law. It should be noted that the presented methodology is independent of the classifier. In our case, we have applied it to an artificial system that captures haptic information from a collection of stimuli. We have verified that the relationship between the perceived distances in the internal representation and the probability of confusion between stimuli follows Shepard’s law. Verifying compliance with this law in artificial systems and studying its implications can be relevant to understanding generalization in learning.