Although diesel engines are being increasingly adopted by many car manufacturers today, no exact mathematical diesel engine model exists due to its highly nonlinear structure. In the current literature, black-box identification has been widely used for diesel engine modelling and many artificial neural network (ANN) based models have been developed. However, ANN has many drawbacks such as multiple local minima, user burden on selection of optimal network structure, large training data size and over-fitting risk. To overcome these drawbacks, this paper proposes to apply one emerging technique, relevance vector machine (RVM), to model the diesel engine, and to predict the engine performance and exhaust emissions. The property of global optimal solution of RVM allows the model to be trained using only a few experimental data sets. In this study, the inputs of the model are the controllable parameters such as engine speed, load and coolant temperature, while the output parameters are the brake specific fuel consumption and exhaust emissions including nitrogen oxides (NOx ) and carbon dioxides (CO2). Experimental results show that the model accuracy is satisfactory even the training data is scarce. Moreover, the model accuracy is compared with that using typical ANN. Evaluation results also show that RVM is superior to typical ANN approach.