At present time the Internet has become a major source of information and a powerful didactic tool. Furthermore, the development of digital equipment, allows to acquire and store large quantities of medical data, including images. In the context of the CISMeF on-line health-catalogue, our work is centered on the automatic categorization of medical images according to their visual content, for further indexation and retrieval tasks. The aim of the present study is to assess the performance of a new image symbolic descriptor for medical modality, anatomic region and view angle image categorization. This descriptor is issued from the unsupervised partition of statistical and texture image subblock representations. A medical image database of 10322 images from 33 classes was ground-truthed by a domain expert. Despite the complexity and variability of medical images, the compact symbolic representation approach proposed in this paper achieves high recognition rates. Thus, using kNN classifiers, we obtain an average precision of 83% and a top performance of 91.19%.