A series of Event-Related Potential (ERP) signals were analyzed with pattern recognition methods using new morphological features and powerful classifiers, in an attempt to develop a computer-aided discrimination system of OCD patients from controls. Eighteen OCD patients and twenty controls were examined. All subjects were evaluated by a computerized version of the digit span subtest of the Wechsler Adult Intelligence Scale. EEGs were recorded from 15 scalp leads. From the P600 component of each signal nineteen waveform-features were calculated. The 3rd-degree Least Squares - Minimum Distance classifier (LSMD3C) and the Support Vector Machines classifier (SVMC) were developed. The LSMD3C was fed with features from all leads and the best feature combinations were inputted into the SVMC to improve the classification results. Highest overall accuracy (89.5%) was found at the C6 lead indicating that OCD patients may present deficits related to working memory mechanisms corresponding to the right temporocentral region.