This article investigates the effects of resolution on the automated segmentation and classification of mammographic masses. A set of 39 mammographic images containing 40 masses are digitized at two resolutions: 220 micrometer and 8 bits per pixel and 180 micrometer and 16 bits per pixel. An expert mammographer classified the shape of all 40 masses as round, lobular, or irregular, and manually segmented the masses from the lower resolution images. The masses in both sets are automatically segmented with a Markov Random Field-based method. Two groups of shape features are extracted from the segmented masses in each set of images: (1) compactness, radial distance mean, standard deviation, entropy, zero- crossing count, and roughness index, and (2) wavelet-based scalar-energy features. Linear discriminant analysis and a minimum Euclidean distance classifier are used to automatically separate the mass shapes into the three classes determined by the expert. The effects of the resolution and method of segmentation on the classification process are analyzed for both groups of shape features.