The segmentation of the breast from the background and the pectoral muscle is the first pre-processing step in computerised mammographic analysis. This problem is usually solved by dividing it into two different segmentation strategies, one for the background and another one for the pectoral muscle. In this paper we tackle this problem jointly using a supervised single strategy. Namely, from a set of manually segmented mammograms, we model each of the three regions (breast, pectoral muscle, and background) using position, intensity, and texture information. Although the approach requires a training step, it allows a fast and reliable segmentation of new mammograms. The obtained results using 149 mammograms of the MIAS database show a high degree of overlap between manual and automatic segmentation.