P300-based GKT (guilty knowledge test) has been suggested as an alternative approach for conventional polygraphy. The purpose of this study is to evaluate three classifying methods for this approach and compare their performances in a lab analogue. Several subjects went through the designed GKT paradigm and their respective brain signals were recorded. For the analysis of signals, BAD (bootstrapped amplitude difference) and BCD (bootstrapped correlation difference) methods as two predefined methods alongside a new approach consisting of wavelet features and a statistical classifier were implemented. The rates of correct detection in guilty and innocent subjects were 74-80%. The results indicate the potential of P300-based GKT for detecting concealed information, although further research is required to increase its accuracy and precision and evaluating its vulnerability to countermeasures.