Bayesian networks have been applied to many different domains in order to perform prognostics, reduce risk and ultimately improve decision making. However, these methods have not been applied to military ground vehicle field data sets. The primary objective of this study is to illustrate how Bayesian networks can be applied to a ground vehicle data set in order to predict potential downtime. The study generated a representative field data set, along with tabu search, in order to learn the network structure followed by quantification of link probabilities. The method is illustrated in a case study and future work is described in order to integrate the method into a real-time monitoring system. The study yielded a highly accurate prediction algorithm that can improve decision making, reduce downtime and more efficiently manage resources in the ground vehicle community.