Abstract In this paper we present the Computer-Aided Library for MAmmography (CALMA Project), i.e. an automated search for the mammograms’ texture, the massive lesions and microcalcifications clusters. CALMA's main purpose is to collect a database of mammographic images, developing CAD tools to be used as a second radiologist in the classification of breast cancer diseases. In this moment, 2200 images are already in our database, which is, to our knowledge, the largest in Europe. One-third of our digitized images are pathological, and they are fully characterized by a consistent description and diagnosis. We try to perform automatically the classification of mammographic images on the bases of tissues’ textures. Such a classification should be done in an unbiased way with respect to radiologists and should support their interpretation of the mammographic image. Results obtained with non supervised neural networks are shown, as well as results coming from a mixed approach (features extraction combined with FF-ANN). Massive lesions are rather large objects to be detected, but they show up with a faint contrast slowly increasing with time. The need for tools able to recognize such a lesion at an early stage is therefore apparent. Our tools are based on a ROI hunter procedure for spiculated lesions showing a number of false positives of the order of 1.4 per image and keeping a 85% sensitivity on our sample. A microcalcication is a rather small (0.1–1.0 mm in diameter) but very brilliant. Some of them, either grouped in cluster or isolated may indicate the presence of a tumor. Up to now only 40 images with microcalcications from our database have been analyzed, and a CAD tool has been designed to detect clusters, reaching a correct classification of 90%.