Traditional statistical process control charts assume that generated process data are normally and independently distributed, i.e. uncorrelated. This research presents the effect of autocorrelation on process control charts to monitor the two quality characteristics of fine coals produced in a coal washing plant for power plant, namely moisture content and ash content. Individual (X) and moving range charts (MR) were constructed to monitor 10 months data. It was determined that even though both data values obey the normal distribution, there is a moderate autocorrelation between their observations. For simulating the autocorrelated data, ARIMA time-series models were used. It was found that X/MR charts showed many false alarms due to the autocorrelation. The ARIMA (1, 0, 1) for moisture content and ARIMA (0, 1, 2) for ash content were determined to be the best models to remove autocorrelation. Compared to large number of false alarms on conventional X/MR charts and on charts applying the Western Electric rules, which assume the data independence, there were much less unusual points on the X/MR charts of residuals (Special Cause Charts). Usage of residual based control charts is suggested when the data are autocorrelated.