Traditionally, control charts are developed assuming that the sequence of process observations to which they are applied are uncorrelated. Unfortunately, this assumption is frequently violated in practice. The presence of autocorrelation has a serious impact on the performance of control charts, causing a dramatic increase in the frequency of false alarms. This paper presents methods for applying statistical control charts to autocorrelated data. The primary method is based on modeling the autocorrelative structure in the original data and applying control charts to the residuals. We show that the exponentially weighted moving average (EWMA) statistic provides the basis of an approximate procedure that can be useful for autocorrelated data. Illustrations are provided using real process data.