Principal component analysis (PCA) is a powerful technique for constructing reduced-order models based on process measurements, obtained by the rotation of the measurement space. These models can be subsequently utilized for chemical-process monitoring, particularly for disturbance and failure diagnosis. Since the standard PCA procedure does not account for the time-dependent relationships among the process variables, this leads to poorer disturbance isolation capability in dynamic applications. A simple idea, in which the last s PCA scores are recursively summed and used to construct descriptive statistics for process monitoring, is presented. Analytically, it is shown that the disturbance resolution afforded is enhanced as a result. Resolution is improved further through the use of an algorithm that enhances the correlations between the input and output variables through optimal time shifting. An overall strategy for on-line monitoring developed includes disturbance identification through mapping. The approach is demonstrated by two industrially relevant case studies.