Classification based on EEG data in an RSVP experiment is considered. Although the latency in neural response relative to the stimulus onset time may be more realistically considered to vary across trials due to factors such as subject fatigue and environmental distractions, it is nevertheless assumed to be time-locked to the stimulus in most of the existing work as a means to alleviate the computational complexity. We consider here a more practical scenario that allows variation in response latency and develop a rigorous statistical formulation for modeling the uncertainty within the varying latency coupled with a likelihood ratio test (LRT) for classification. The new model not only improves the EEG classification performance, but also may predict the true stimulus onset time when this information is not precisely available. We test the proposed LRT algorithm on an EEG data set from an image RSVP experiment and show that, by admitting the latency variation, the proposed approach consistently outperforms a method that relies on perfect time-locking (AUC: 0.88 vs 0.86), especially when the stimulus onset time is not precisely available (AUC: 0.84 vs 0.71). Furthermore, the predicted stimulus onset times are highly enriched around the true onset time with p-value = 5.2 x 10(44).