Purpose: The prognosis of melanoma, an invasive and malignant skin tumor, strongly relies on early detection. Unfortunately differentiating early melanomas from other less dangerous pigmented lesions is a difficult task even for trained observers since they may have near physical characteristics. Dermatoscopy, a new non-invasive technique which makes subsurface structures of skin accessible to in vivo examination provides standardized images of black tumors that seem convenient for numerical analysis. The objective of this project is to develop a computer-based diagnostic system which takes advantage of dermatoscopic images to characterize black tumors and help to detect melanoma. Methods: Dermatologists ground their diagnosis on the observation of some characteristic features in images of black tumors. Similarly, our approach consists in classifying parts of images of skin tumors (called windows thereafter) by a two-stage procedure. First, a contextual coding of widows is achieved by GHA network (Generalized Hebbian Algorithm). The second stage involves a classical feedforward network (a multilayer perceptron) which performs a classification of coded windows. Both stages rely on learning to achieve their task. The GHA network operates a Principal Component-like analysis of windows. During that phase, sets of primitive images fitted to various contexts are constituted, each set being appropriate for the description of some aspects of the windows (contrast, texture, border, color, ...). Windows can subsequently be coded by projection on these bases. Finally, a supervised learning is carried out to build up the classifier, using parts of characterized images with respect to the features under consideration. Results: Most of the interesting features detectable in black tumors can be observed in 16*16 pixel windows, providing resolution is properly chosen. The analysis of such windows by our system shows that classification is properly achieved when 20 primitive windows at least are considered for windows coding. Preliminary results dealing with the detection of several features of lesions have been found encouraging. Conclusion: The use of model-free approach, directly applied on image and based on learning by sample has been found efficient. Adapting this approach to the detection of melanoma appears a promising way.