One of the fundamental tasks facing autonomous robots is to reduce the many degrees of freedom of the input space by some sort of classification mechanism. The sensory stimulation caused by one and the same object, for instance, varies enormously, depending on lighting conditions, distance from object, orientation, and so on. Efforts to solve this problem, say, in classical computer vision, have had only limited success. In this article a new approach toward classification in autonomous robots is proposed. Its cornerstone is the integration of the robots' own actions into the classification process. More specifically, correlations through time-linked independent samples of sensory stimuli and of kinesthetic signals produced by selfmotion of the system form the basis of the category learning. Thus it is suggested that classification should not be seen as an isolated perceptual (sub)system but rather as a sensory-motor coordination that comes about through a self-organizing process. These ideas are illustrated with a case study of an autonomous system that has to learn to distinguish between graspable and nongraspable objects.