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This paper presents an interval type-2 fuzzy K-nearest neighbor (NN) algorithm that is an extension of the type-1 fuzzy K-NN algorithm proposed in. In our proposed method, the membership values for each pattern vector are extended as interval type-2 fuzzy memberships by assigning uncertainty to the type-1 memberships. By doing so, the classification result obtained by the interval type-2 fuzzy K-NN is found to be more reasonable than that of the crisp and type-1 fuzzy K-NN. Experimental results are given to show the effectiveness of our method.