Off-road vehicle maintenance is getting increasingly important as the unplanned stops might significantly damage the entire work delaying the process beyond the limits. While preventive maintenance consists of replacing periodically vehicle components, predictive maintenance predicts if and when a failure is going to occur. This thesis investigates unsupervised and supervised methods for predicting vehicle maintenance. In order to achieve this goal, we have used probabilistic methods based on the concept of Markov chains (HMMs), distance (kNN) and tree (Isolation Forest) based algorithms, and finally deep neural networks (Autoencoders). We built a model reaching about 81% of F1 score in the prediction of diagnostic messages occurrences.