In nearly all papers on process control for time-dependent data, it is assumed that the underlying target process is stationary. In the present paper, the target process is modeled by a multivariate state-space model which may be non-stationary. Our aim is to monitor its mean behavior. The likelihood ratio method, the sequential probability ratio test and the Shiryaev–Roberts procedure are applied to derive control charts signaling a change from the supposed mean structure. These procedures depend on certain reference values which have to be chosen by the practitioners. The corresponding generalized approaches are considered as well, and generalized control charts are determined for state-space processes. These schemes do not have further design parameters. In an extensive simulation study, the behavior of the introduced schemes is compared with each other using various performance criteria like the average run length, the average delay, the probability of a successful detection, and the probability of a false detection.