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A major issue in k-nearest neighbor classification is how to choose the optimum value of the neighborhood parameter k. Popular cross-validation techniques often fail to guide us well in selecting k mainly due to the presence of multiple minimizers of the estimated misclassification rate. This article investigates a Bayesian method in this connection, which solves the problem of multiple optimizers. The utility of the proposed method is illustrated using some benchmark data sets.