The aim of this study was to design a computer-based classification system capable of distinguishing one-month abstinent alcoholics from normal controls by Event-related potential (ERP) signals using the P600 component. Clinical material comprised twenty one-month abstinent alcoholics and an equal number of gender and aged-matched healthy controls. All subjects were evaluated by a computerized version of the digit span Wechsler test. EEG activity was recorded and digitized from 15 scalp electrodes (leads). A dedicated computer software was developed and it was used to read the ERP signals and to calculate features related to the P600 component (500-800 ms) of the ERP signal. Nineteen features were generated and were employed in the design of an optimum SVM-classifier at each lead. The outcomes of those SVM-classifiers were selected by a majority-vote engine (MVE), which assigned each subject to either the normal or alcoholic classes. MVE-classification accuracy was 97.5% when using all leads and 92.5% or 77.5% when using only the right or left scalp leads respectively. These findings provide evidence of right hemisphere dysfunction in alcoholics affecting the processing of information that assigns a specific response to a specific stimulus, as those mechanisms are reflected by the P600 component of ERPs.