In general, nearest-neighbor methods classify an object based on the group membership of the training observations within a certain neighborhood of the object in question. These methods share both the advantages and the disadvantages of other methods for distribution-free inference. In this talk a two-stage nearest-neighbor classifier is proposed which attempts to exploit the advantages of the (single-stage) nearest-neighbor classifier while simultaneously reducing the extent to which the classifier is overfit to the training data. This present work is motivated by the problem of microbial source tracking, which attempts to trace the source of bacterial pathogens in water resources using genetic fingerprints. Applications of the proposed methodology to real and simulated data will be presented as time permits.