Startup, shutdown and other transitions are integral to batch and continuous process operations. Operators usually execute transitions in manual mode. Processes are therefore prone to operator errors in addition to process faults during transitions. If undetected, such abnormalities can lead to process downtime and in the worst case, accidents. Although essential, fault detection during transitions has received little attention in literature. This paper presents a novel multiple filters and observers based fault detection scheme using (i) a nonlinear process model, and (ii) knowledge of the standard operating procedure for executing the transition. Extended Kalman filters, Kalman filters, and open-loop observers are used to estimate process states during the transition and generate residuals. These residuals indicate deviations from normal operation due to process faults and operator errors. The model-based scheme has been implemented in Matlab/Simulink and found to successfully detect faults during the startup of an experimental pH neutralization CSTR.