This paper introduces a novel statistical approach for recognizing handwritten Arabic characters. The proposed method involves, as a first step, digitization of the segmented character. The secondary characters are then isolated and identified separately thereby reducing the recognition issue to a 20 class problem. The moments of the horizontal and vertical projections of the remaining primary characters are estimated and normalized with respect to the zero order moment. Simple measures of shape are obtained from the normalized moments and incorporated into a feature vector. Classification is accomplished using quadratic discriminant functions. Results confirming that the method show considerable merit are presented.