Abstract : According to clinical protocols, skin diseases are quantified by dermatologists throughout a treatment period, and then a statistical test on these measures allows to evaluate a treatment efficacy. The first step of this process it to classify pathological interest areas. This task is challenging due to the high variability of the images in one clinical data set. In this report, we first review algorithms that exist in the literature and adapt them to our problem. Then we choose the more appropriate algorithm to design a classification strategy. Thereby, we propose to use data reduction combined with SVM to do a first classification of the disease. Then we associate the obtained classification map with a segmentation map in an "interactive classification tool" in order to compromise between operator dependency and algorithm robustness.