For radiologists lesion margin appearance is of high importance whenclassifying breast masses as malignant or benign lesions. In thisstudy, we developed different measures to characterize the margin of alesion. Towards this goal, we developed a series of algorithms toquantify the degree of sharpness and lobulation of a massmargin. Besides, to estimate spiculation of a margin, featurespreviously developed for mass detection were used. Images selectedfrom the publicly available data set "Digital Database for ScreeningMammography" were used for development and evaluation of thesealgorithms. The data set consisted of 777 images corresponding to 382patients. To extract lesions from the mammograms a segmentationalgorithm based on dynamic programming was used. Features wereextracted for each lesion. A k-nearest neighbor algorithm was used incombination with a leave-one-out procedure to select the best featuresfor classification purposes. Classification accuracy was evaluatedusing the area Az under the receiver operating characteristic curve. The average test Az value for the task of classifying masses on a single mammographic view was 0.79. In a case-based evaluation we obtained an Az value of 0.84.