Abstract: A pattern recognition system classifies images of patterns in which the definition of individual features of the pattern may have become blurred. The image is segmented into pieces of arbitrary size and shape, and various combinations are examined to determine those which represent the most likely segmentation of the pattern into its individual features. These individual features are then classified, according to known techniques. Through the use of a second order Markov model, not all possible combinations of pieces need to be examined, to determine the best ones. Rather, the examination of various combinations is limited in accordance with previously determined information, to thereby render the process more efficient. By combining multiple, independently determined probabilities, the accuracy of the overall operation is enhanced.