Markovian analysis is a method to measure optical texture based on gray-level transition probabilities in digitized images. Experiments are described that investigate the classification performance of parameters generated by Markovian analysis. Results using Markov texture parameters show that the selection of a Markov step size strongly affects classification error rates and the number of parameters required to achieve the maximum correct classification rates. Markov texture parameters are shown to achieve high rates of correct classification in discriminating images of normal from abnormal cervical cell nuclei. Texture is one of several properties (e.g., color, shape, size) by which human beings perceive images, and it is an important property for the classification of biomedical images. I This work was performed in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Biomedical Engineering,