This paper presents a series of experiments testing the feasibility of employing image-processing techniques for the feature extraction stage in the implementation of a basic optic nerve image classifier. Such a scheme completely removes the need for manually identifying the edge of the optic nerve. In this work, Zernike moments are extracted from Confocal Scanning Laser Tomography images of optic discs for the purposes of classifying the disc as healthy or damaged using a linear discriminant function derived from a linear perceptron. Our preliminary results, when compared with the performance of conventional feature sets, demonstrate the appropriateness of this approach.