One of the critical problems in the operation of chemical process is occurrence of abnormal events. Therefore a process monitoring system that can detect and diagnose abnormal events is important for effective and stable operation of chemical process. A new type of timed fuzzy Petri net（tFPN）approach to prognostication and diagnosis of abnormal events was proposed in this paper. In tFPN，a timing factor was associated with the transition and the degree of truth of rule，with which the reliabilities and appearing times of abnormal events could be inferred automatically. The procedures of abnormal events monitoring based on tFPN models were presented in detail. Finally，the proposed techniques and solutions were demonstrated through a polypropylene reactor case study，which showed promising results by prognostication of abnormal events and diagnosis of root causes.