The Rail Safety and Standards Board (RSSB) Safety Risk Model (SRM) provides a structured representation of the causes and consequences of potential accidents arising from railway operations and maintenance on the Great Britain railway network (GBRN). It consists of a series of fault tree and event tree models representing 120 hazardous events that collectively define an overall level of risk on this system.In 2003–2009, RSSB jointly undertook research with Strathclyde University into developing a method of calculating uncertainty that could be used on the SRM's models. Since these models are built using fault and event tree logic, they consist of many branches of related variables. This creates a significant challenge in terms of calculating uncertainty, as there are many interrelated factors to take into account when constructing the uncertainty model.The research has looked at various ways of eliciting correlation and covariance matrices that can be used to simplify the uncertainty analysis while still taking account of the interdependency between variables. These methods are used in conjunction with a Monte-Carlo simulation to sample from distributions and produce data samples that can be used to populate the SRM models. These models are then run many times using the samples, in order to build up distributions of expected risk results. They can then be used to determine confidence intervals on the risk figures.This research is now complete and a case study on one of the hazardous events of the SRM has been carried out. The purpose of this paper is to discuss the application of the uncertainty models to the SRM and to present the results that have been obtained using this uncertainty method.