Autocorrelation in process measurements results in false alarms when traditional control charts, such asX̄andRcharts, are applied in process monitoring. A popular approach proposed in the literature is to use time series models to remove the autocorrelation, and then to apply control charts to the residuals, or prediction errors. However, when a time series model is used to remove the autocorrelation, the change patterns may be severely altered, making the interpretation of a process change and root cause analysis erroneous. This paper develops charts for the comparison of change patterns for two types of additive process faults before and after applying time series modeling. The process changes are sustained mean shifts and sporadic spikes. These charts provide improved understanding of the characteristics of autocorrelated processes.