This dissertation describes a procedure for segmenting imagery using digital techniques and is based on the mathematical model. The classifer does not require training prototypes, that is, it operates in an unsupervised mode. The procedure is general in that the features most useful for the particular image to be segmented are selected by the algorithm. The algorithm operates without any human interaction. The features used are based on brightness and texture in regions centered on every picture element in the image. To perform an elementary pre-classification of local regions, a filter based on the mode of the local area histogram is proposed and used in segmenting images. The basic procedure is a K-means clustering algorithm which converges to a local minimum in the average squared inter-cluster distance for a specified number of clusters. The algorithm iterates on the number of clusters, evaluating the clustering based on a parameter of clustering quality. The parameter proposed is a product of between and within cluster scatter measures, which achieves a maximum value that is postulated to represent an intrinsic number of clusters in the data.