The basic assumption of traditional quality control charts is that data taken from the process are independent and identically distributed. However, the independence assumption is often not valid in practice as autocorrelation amongst the data becomes an inherent characteristic in many processes. Since any false judgment about process stability causes unnecessary interventions to process, it is important to investigate the independence assumption firstly and then to use the suitable control chart type. If autocorrelation is recognized in data, appropriate time series model can be used to model the correlative structure and then control charts can be applied to the independent and identically distributed stream of the residuals. This kind of control charts are called as Special Cause Control (SCC) Charts. In this study, SCC chart was compared with the outcomes of Individuals Control (IC) Chart. In the presence of autocorrelation, while IC chart gave a lot of false signals of special cause variation, SCC chart gave three signals of special cause variation.