In this paper, we present a neuromorphic character recognition application using a memristor crossbar architecture. The tested memristor crossbar architecture was composed of a single crossbar array with bipolar inputs for performing the Exclusive-NOR function to measure the similarity between the input character and the pre-stored characters. Ten characters, from "A" to "J", were used for testing the crossbar circuit. For being recognized, ten characters were converted to vectors and applied to the single memristor array with the size of 64×10, which pre-stored all character images, to find the similarity score between the input character and the characters stored in the array. The winner-take-all circuit finally selected the maximum current of the column which stored the pattern that was the best match with the input character. The memristor variation tolerance of the single crossbar array was verified in this work. The simulation results indicate that the single crossbar array with bipolar input performs precisely the Exclusive-NOR function between the input pattern and the patterns stored in the array as well as tolerates well the variation in memristance.