Event-Related Potentials (ERPs) provide noninvasive measurements of the electrical activity on the scalp related to the processing of stimuli and preparation of responses by the brain. In this paper, an ERP-signal classification method capable of discriminating between ERPs of correct and incorrect responses of actors is proposed. A number of histogram-related features were calculated from each ERP-signal and the most significant ones were extracted using the Sequential Forward Floating Selection algorithm along with the Fuzzy C-Means clustering algorithm. The Fuzzy C-Means algorithm was also used for the classification task. The approach yielded classification accuracy 93.75% for the actors’ correct and incorrect responses. The proposed ERPsignal classification method provides a promising tool to study error detection and observational-learning mechanisms in joint-action research and may foster the future development of systems capable of automatically detecting erroneous actions in human-human and human-artificial agent interactions.