The complexities of process plants are increasing because of process integration and plant-wide optimization. Failure models of a process system (henceforth referred to as process-accident models) should be able to capture the inherent dependence among process components and their associated variables and also the temporal dependencies among failures. This work demonstrates the suitability and applicability of process-accident models in capturing temporal dependence using process data. Performances of process-accident models are investigated to establish their competitive advantages as well as their limitations. Using experimental data from a pilot plant, the performances of three widely used accident models, namely, the fault tree (FT), the dynamic fault tree (DFT), and the dynamic Bayesian network (DBN), are evaluated in predicting abnormal events. Normal and abnormal process data is collected and used in studying the three different models to assess the process-accident probability. The study confirmed the DBN model to be the most appropriate accident-modeling approach because of its flexible structure and ability to capture spatial and temporal dependencies.