Computerized pattern recognition was used (or discriminating between complicated and uncomplicated electrode processes and for Identifying the mechanism of the electrode process using the shape Information contained In the data from a single voltammetric experiment. Features based on the Fourier transform, which were selected to give the best classification accuracy with theoretical cyclic linear sweep voltammetry (CLSV) data, could be used to classify experimental CLSV or cyclic staircase voltammetry (CSCV) data. The theoretical data were classified with an accuracy of 97%. The experimental data were correctly classified 93% of the time when the shape of the voltammogram was determined by a single mechanism which was Included In the theoretical training set.