This paper presents an interval type-2 fuzzy perceptron algorithm that is an extension of the type-1 fuzzy perceptron algorithm proposed by J. Keller et al. (1985). 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 decision boundary obtained by interval type-2 fuzzy memberships can converge to a more desirable location than the boundary obtained by crisp and type-1 fuzzy perceptron methods. Experimental results are given to show the effectiveness of our method.