We have developed a reject option for VQ-based supervise d Bayesian classification to improve classification accuracy by sieving out patterns that are classified with a low confidence value. A small codebook extracted from a learning vector quantizer (LV Q) is used to estimate the class-conditional densities of the feature vector. We adapt the two commonly used rejection criteria, outlier rejection and ambiguity rejection, for the V Q-based Bayesian classifiers. Using three high-level image classification problems, we demonstrate how local rejection criteria can improve the error vs. reject characteristics of our classifier over a global rejection method.