The aim of the paper is to describe a decision support system operating in the area of capillaroscopic images. The system automatically sites the capillaroscopic analyzed image into one of the following classes: normal, diabetic and sclerodermic. The automatic morphometric analysis attempts to imitate the physician behavior and requires the introduction of some particular features connected with the specific domain. These features allow a symbolic representation of the capillary partitioning it into three components: apex, arteriolar, and venular. Each component is qualified by specific attributes which allow the necessary shape evaluations in order to discriminate among the classes of capillaries. The system is hierarchically organized in two levels. The first level is concerned with the segmentation after a noise reduction and an enhancement of the digitized image. This level uses a shell, developed and successfully experimented for many heterogeneous classes of images. The second level is concerned with the effective classification of the previously processed image. It matches the visual data with a model constituted by a semantic network which embeds the geometric and structural a-priori knowledge of all kinds of capillaries. The system has been successfully used in experiments to obtain images of nailfold capillaries of the human finger.