The extended Kalman filter (EKF) is one of the most popular model-based techniques for fault detection and diagnosis. Although its effectiveness has been widely recognized, the practical applications of EKFs are still very limited. This is due to the fact that the estimates of EKF are often biased when the occurrence of multiple faults is possible. In this study, we have extended the findings of our previous research on diagnostic observability and diagnostic resolution concerning a set of parallel single-parameter EI;Fs (Chang et al., 1993, A.I.Ch.E. J. 39, 1146) to the multiple-parameter EKFs which are designed to identify more than one fault origin. The problems in implementing these EKFs, i.e. misdiagnosis due to biased estimates and heavy computation load due to the parallel configuration, have been solved with a selection strategy for proper combinations of sensor locations and EKF parameters. More importantly, simple procedures have been developed to quickly evaluate the performance of any given system and the reliability of the proposed approach has been repeatedly confirmed in extensive simulation studies without exceptions. As a result, it becomes feasible to construct appropriate fault monitoring schemes without extensive computational effort even for large and complex chemical processes.