Unsteady state startup of chemical processes has such characters as nonlinearity, large variation of parameters, multiple abnormal states, etc., which failures may cause serious damage to the atmospheric environment due to uncontrolled pollutant emissions. Fault diagnosis facilitates early detection of abnormal symptoms and timely determination of abnormal reasons during startup procedure. This paper proposed a hybrid fault diagnosis strategy based on dynamic simulation and dynamic time warping (DTW) to probe transient degradation of startup performance. It calculates serial residuals between dynamic simulation values and online measured ones using DTW method to represent process development trend. The residual vector under normal conditions is used to develop principal component analysis (PCA) model. Measurements in startup process pretreated by DTW are input into the PCA model to perform fault detection and isolation work at last. The proposed method was applied to a penicillin fermentation simulator to verify its feasibility in unit operations. Case studies demonstrate that the fault diagnosis strategy allows a fast and effective supervision of abnormal state during chemical process startups.