In nationwide mammography screening thousands of mammography examinations must be processed. Each consists of two standard views of each breast, and each must be examined by the eyes of an experienced radiologist to determine whether or not to recall the subject, or to undergo a closer mammographic, ultrasound and or more invasive examination. The eyes of the very experienced radiologist are alerted to a mammogram that deserves to be recalled and it is submitted that an ability to pick out anomaly of texture in mammography screening is a very important characteristic of successful outcomes in screening. We digitized a large number of pristine mammography images with a highly accurate scanner and processed textural statistics derived from 450 of these images through a SONNET self-organizing neural network to produce an organization of the mammography archive. In this paper we describe some aspects of this work. The best result produced 39 stable classes and produced relatively narrow classes with an average within-class distance of 1.01 whilst retaining a typical average between-class distance of 2.00. The chief features that discriminated class encodings were the two textural features: angular second moment and contrast. The search for multiscale features over a diverse set of mammograms represents a very challenging problem owing to the high dimensionality of the potential search space.