RASTEP (RApid Source TErm Prediction) is a computerized tool for use in the fast diagnosis of accidents in nuclear power plants and analysis of the subsequent radiological source term. The tool is based on a Bayesian Belief Network that is used to determine the most likely plant state which in turn is associated with a pre-calculated source term from level 2 PSA. In its current design the source term predicting abilities of RASTEP are not flexible enough. Therefore, the purpose of this thesis is to identify and evaluate different approaches of enhancing the source term module of RASTEP and provide the foundation for future implementations. Literature studies along with interviews and analysis have been carried out in order to identify possible methods and also to rank them according to feasibility. 4 main methods have been identified of which 2 are considered the most feasible in the short term. The other 2 might prove useful when their maturity level is strengthened. It is concluded from the study that the identified methods can be used in order to enhance RASTEP.