The statistical cumulative sum (CUSUM) chart is a powerful tool for monitoring the attribute quality variable in manufacturing industry. In this article, we studied the multiplicity problem caused by simultaneously monitoring more than one attribute quality variable. Multiple binomial and Poisson CUSUM charts incorporating a multiple hypothesis testing technique known as false discovery rate control were proposed. The procedures for establishing the new control schemes were presented, and the performance of the new methods was evaluated using Monte Carlo simulation. The approximation methods for obtaining the p-values of the CUSUM statistics for conducting the new control schemes were also provided and evaluated. The new methods were also illustrated with a real example. Copyright © 2011 John Wiley & Sons, Ltd.