An extended partial-least squares (EPLS) algorithm is introduced to correct a deficiency of conventional partial least squares (PLS) when used as a tool to detect abnormal operating conditions in industrial processes. In the absence of feedback control, an abnormal operating condition that affects only process response variables will not be propagated back to the process predictor (or input) variables. Thus monitoring tools developed under the conventional PLS framework and based only on the predictor matrix will fail to detect the abnormal condition. The EPLS algorithm described removes this deficiency by defining new scores that are based on both predictor and response variables. The EPLS approach provides two monitoring charts to detect abnormal process behavior, as well as contribution charts to diagnose this behavior. To demonstrate the utility of the new approach, the extended algorithm and monitoring tools are applied to a realistic simulation of a fluid catalytic cracking unit and to a real industrial process that involves a complex chemical reaction.