In this investigation ^number of image texture analysis techniques for the classification of microcalcifications in digitised mammograms are presented. Microcalcifications are often an early indication of breast cancer, and computer-aided diagnostic techniques are capable of improving diagnostic accuracy. Three categories of image texture features are extracted from regions of interest surrounding clusters of microcalcifications. These comprise a set of statistical texture features based on the co-occurrence matrix, a set of wavelet-based texture signatures and a propose^ third set of texture features. This set, referred to as multiscale statistical texture features, is based on a combination of the other two approaches to texture analysis. The multiscale statistical texture features outperform the other types of texture features in tests using two separate datasets and a k-nn classifier for classification. Improved classification accuracy is also achieved using an artificial neural network for classification.