Retinal prostheses work by delivering electrical pulses to the surviving retinal neurons. A pattern of electrical stimulation can generate a perception of vision in blind patients. To improve efficacy of retinal implants, it is important to understand how different classes of retinal neurons respond to electrical stimulation and if a classification can be made based on the electrophysiological properties of neurons. We use previously recorded patch clamp data from retinal ganglion cells classified into morphological classes (A,B,C, D) and functional types (ON, OFF, ON-OFF). We use a machine learning technique to separate data based on the recorded electrophysiological parameters. Results show that the clusters discovered using the machine learning technique do not correspond to the morphological or functional classes used by neuroscientists.