This article introduces a technique for monitoring chemical processes that are driven by a set of serially correlated nonstationary and stationary factors. The approach relies on (i) identifying and separating the common stationary and nonstationary factors, (ii) modeling these factors using multivariate time-series models, and (iii) incorporating a compensation scheme to directly monitor these factors without being compromised by the effect of forecast recovery. Based on the residuals of the time-series models, the technique yields two distinct test statistics to monitor both types of factors individually. In contrast to existing works, this article highlights that the technique is sensitive to any fault condition and can extract and describe both stationary and nonstationary trends. These benefits are illustrated by a simulation example and application of the approach to an industrial semibatch process describing nonstationary emptying and filling cycles.