In this paper, some of the factors that affect classification performance of EEG based Brain-Computer Interfaces (BCI) is studied. Study is specified on P300 speller system which is also an EEG based BCI system. P300 is a physiological signal that represents a response of brain to a given stimulus which occurs right 300ms afier the stimulus onset. When this signal occurs, it changes the continuous EEG some micro volts. Since this is not a very distinguished change, some other physiological signals (movement of muscles and heart, blinking or other neural activities) may distort this signal. In order to understand if there is really a P300 component in the signal, consecutive P300 epochs are averaged over trials. In this study, we have been tried two different multi channel data handling methods with two different frequency windows. Resulted data have been classified using Support Vector Machines (SVM). It has been shown that proposed method has a better classification performance.