Several types of accidents, such as exposure to toxic gases, fires, or explosions, are encountered in process industries which are highly risk systems. To reduce risks and the consequences of disruptive events, resilience is recognized as one of the most important aspects of safety management, and resilience assessment in complex process systems plays an important role. This study examines methods for resilience assessment in process industries, by reviewing the published studies. Given the transient changes in resilience and performance variability due to complexity, it is examined which methods are more commonly applied for quantitative resilience assessments. As a result of the review of published literature, the most commonly used method to assess resilience in process industries is Dynamic Bayesian Network (DBN). DBN may be used for the estimation of uncertainty and probability of resilience in chemical processes. The resilience of complex process systems, which consider some aspects of resilience like absorption, adaptation, and recovery, is addressed and modeled by DBN. This review provides information on the use of quantitative methods to assess the resilience of complex process systems, the estimation of failure probability, the determination of performance variability under complex conditions, and the model of interactions between the components of a complex process system.