As neural interfaces continue to progress toward practical applications, there is increased demand for smaller, more efficient and cost effective devices. Event related potentials (ERPs) have recently been demonstrated to be reliable for practical communication in disabled individuals using the P300 Speller paradigm. With the objective of simplifying the processing of ERPs in order to minimize the hardware/computational requirements, and therefore the power consumption (for increased battery life for wireless, etc.), this study examines the effects of the analog-to-digital converter amplitude quantization on the ERP classification accuracy for the P300 Speller.