In this paper we present two supervised pattern classifiers designed using Boolean neural networks. They are: 1) nearest-to-an-exemplar classifier; and 2) Boolean k-nearest neighbor classifier. The emphasis during the design of these classifiers was on simplicity, robustness, and the ease of hardware implementation. The classifiers use the idea of radius of attraction to achieve their goal. Mathematical analysis of the algorithms presented in the paper is done to prove their feasibility. Both classifiers are tested with well-known binary and continuous feature valued data sets yielding results comparable with those obtained by similar existing classifiers.