Statistical Process Control (SPC) uses statistical methods to improve the quality of a process. The main tools of SPC are the control charts. Control charts are widely used to distinguish whether a process is in a state of statistical control or not. Control charts are designed and evaluated under the assumption that the observations from the process are independent and identically distributed (iid) normal. However, the independence assumption is often violated in practice. Autocorrelation may be present in many processes. and may have a significant effect on the properties of the control charts. Thus, traditional SPC charts are inappropriate for monitoring process quality. In this study, we present methods for process control that deal with autocorrelated data and mainly a more sophisticated method based on time — series ARIMA models (Alwan —Roberts method). An application is also presented concerning some quality characteristics of water for human consumption. This application makes clear the detrimental effects of autocorrelation when standard SPC control charts are applied on autocorrelated observations.