We apply the support vector machines to classify the P300 signal, and combine several trials to determine the final character. Features are selected from 10 electrodes and a limited time latency, and then downsampled for dimensionality reduction.  For each trial, SVMlight is used to classify the presence/absence of P300 and to assign a score. The scores of mulated and the row/column with the highest score is identified.