This paper presents a fault detection and diagnosis methodology based on the weighted symptom tree (WST) and pattern matching between the coming fault propagation trend and the simulated one. In the first step, backward reasoning is used to find the possible cause candidates for the faults. The weighted symptom tree is used to generate these candidates. The weights are determined by dynamic simulations. By using a weighted symptom tree, the suggested methodology can generate the cause candidates and rank them according to their likelihoods of occurrence. In the next step, the fault propagation trends identified from the partial or complete sequence of measurements are compared to the standard fault propagation trends, which have been generated using dynamic simulation and stored a priori. A pattern matching algorithm based on a number of triangular episodes is used to match those trends effectively. The proposed methodology was illustrated using the Tennessee Eastman challenge process and showed enhanced diagnostic resolution.