In statistical process control it is usually assumed that the observations taken from the process are independent. However, for many processes the observations are autocorrelated, and this autocorrelation can have a significant effect on the performance of traditional control chart. The problem of monitoring the mean of process in which the observations can be modeled as an AR(1) process was considered. A one-step forecasting autocorrelated control charts based on dynamic Bayesian model was presented for engineering practices. The principle and methodology to build autocorrelated control charts was described in detail. This control chart′s performance was compared to the performance of control chart based on process model, and the effect of process parameter estimation on the control chart was also investigated. When the process model is strong autocorrelation AR(1), the autocorrelated control chart has good performances with few samples. In this case, autocorrelated control chart was suggested instead of traditional control charts.