In this paper, we describe a system for detecting encephalography (EEG) signatures of visual recognition events evoked in a single trial during rapid serial visual presentation (RSVP). In order to investigate the viability of nonlinear approaches in EEG detection and assess the performance comparison, we applied three classifiers (linear logistic regression model, Laplacian classifier, and spectral maximum mutual information projection) in the detection tasks. The EEG was recorded using 32 electrodes during the rapid image presentation (50ms/100ms per image). Subjects were instructed to push a button when they recognize a target image. The results suggest that while the detection of single trial EEG-based recognition is possible, taking advantage of the nonlinear techniques requires data representation that would overcome the non-stationarity of the EEG signals.