Summary: The \(K\)-Nearest Neighbor (\(K\)-NN) voting scheme is widely used in problems requiring pattern recognition or classification. In this voting scheme an unknown pattern is classified according to the classifications of its \(K\) nearest neighbors. If a majority of the \(K\) nearest neighbors have a given classification \(C^*\), then the unknown pattern is also given the classification \(C^*\). Although the scheme works well it is sensitive to the number of nearest neighbors, \(K\), which is used. In this paper we describe a fuzzy \(K\)-NN voting scheme in which effectively the value of \(K\) varies automatically according to the local density of known patterns. We find that the new scheme consistently outperforms the traditional \(K\)-NN algorithm.