Most implementations of Bayesian classification assume fixed a priori probabilities. These implementations can be placed into two general categories: (1) those that assume equal a priori probabilities and (2) those that assume unequal but fixed a priori probabilities. We report here on results of classifying polarimetric SAR images using a scheme in which the classification is done iteratively. The first classification is done assuming fixed (but not necessarily equal) a priori probabilities. The results of this first classification are then used in successive iterations to change the a priori probabilities adaptively. The results show that only a few iterations are necessary to improve the classification accuracy dramatically.