The high complexity existent in businesses has required managers to rely on accurate and up to date information. Over the years, many tools have been created to give support to decision makers, such as discrete event simulation and artificial neural networks. Both tools have been applied to improve business performance; however, most of the time they are used separately. This research aims to interpret artificial neural network models that are applied to the data generated by a simulation model and determine which inputs have the most impact on the output of a business. This would allow prioritization of the variables for maximized system performance. A connection weight approach will be used to interpret the artificial neural network models. The research methodology consisted of three main steps: 1) creation of an accurate simulation model, 2) application of artificial neural network models to the output data of the simulation model, and 3) interpretation of the artificial neural network models using the connection weight approach. In order to test this methodology, a study was performed in the raw material receiving process of a manufacturing facility aiming to determine which variables impact the most the total time a truck stays in the system waiting to unload its materials. Through the research it was possible to observe that artificial neural network models can be useful in making good prediction about the system they model. Moreover, through the connection weight approach, artificial neural network models were interpreted and helped determine the variables that have the greatest impact on the modeled system. As future research, it would be interesting to use this methodology with other data mining algorithms and understand which techniques have the greatest capabilities of determining the most meaningful variables of a model. It would also be relevant to use this methodology as a resource to not only prioritize, but optimize a simulation model.