Research over the last decade has shown that brain-computer interfaces (BCI) based on electroencephalography (EEG) can provide an alternative input paradigm for both clinical and healthy populations. Currently, the majority of BCI paradigms rely on a limited number of brain potentials; thus there remain many EEG signals to be explored for BCI applications. One such signal is the N2pc event-related potential (ERP). The N2pc is an ERP elicited 150ms to 350ms post-stimulus onset in response to target detection in visual search tasks. During this time window, target detection causes a negative deflection in the ERPs measured contralaterally to the target, allowing the lateralization of the target to be determined. Here we explore the feasibility of an N2pc-based BCI paradigm by analyzing the classification performance of participants based on data collected during an N2pc elicitation task. We quantify performance as a function of two variables; channel selection and the number of trials averaged together to obtain the ERP. Preliminary results indicate that with as few as three trials, the N2pc can be classified at nearly 90% accuracy in some individuals. These results could directly lead to the development of a new BCI paradigm, which we plan to realize in future work through the construction of a speller interface.