Abstract
Multivariate autocorrelated processes violate the fundamental assumption of independence for traditional statistical process control. This paper investigates bivariate autocorrelated processes in which the observations of one characteristic are autocorrelated following a first-order autoregressive model while the observations of the other characteristic are independent. The system parameters were monitored using an analysis for bivariate autocorrelated processes, termed the residual-based T(2) control chart. The average run lengths predicted by Monte Carlo simulations were given for acluster of such processes with various mean shifts. Analysis of the result shows that the autocorrelation and mean shifts determine whether the residual-based T(2) control chart can be applied and that this chart can efficiently monitor most such processes.