In tissue counter analysis, complex histologic sections are overlaid with regularly distributed measuring masks of equal size and shape, and the digital contents of each mask (or tissue element) are evaluated by gray level, color, and texture parameters. In this study, the feasibility of tissue counter analysis and classification and regression trees for the quantitative evaluation of skin biopsies was assessed. From 100 randomly selected skin biopsies, a learning set of tissue elements was created, differentiating between cellular elements, collagenous elements of the reticular dermis, fatty elements and other tissue components. Classification and regression trees based on the learning set were used to automatically classify tissue elements in samples of normal skin, benign common nevi, malignant melanoma, molluscum contagiosum, seborrheic keratosis, epidermoid cysts, basal cell carcinoma, and scleroderma. The procedure yielded reproducible assessments of the relative amounts of tissue components in various diagnostic groups. Furthermore, a reliable diagnostic separation of molluscum contagiosum versus normal skin and epidermal cysts, benign common nevi versus malignant melanoma, and seborrheic keratosis versus basal cell carcinoma was possible. Tissue counter analysis combined with classification and regression trees may be a suitable approach to the fully automated analysis of histologic sections of skin biopsies.