The search for improvements in fuel economy, specific power, exhaust emissions and refinement leads to an increasing number of experimental variables and flexibility in their operating strategy. This makes identification of optimum settings and robust solutions more demanding. New test and analysis techniques are required to identify underlying trends and sensitivities in order to determine, with some confidence, the technical and commercial benefits of changes to one or more parameters. Greater emphasis on experimental design will therefore be essential for any engine research and development programme.Design of experiments (DoE) techniques have been widely used for identifying optimum settings. Traditional experimental designs can be improved by including engineering knowledge more directly into the design process and by providing information on a continuous basis during the test programme.This paper explores the potential of Bayesian methods in engine Research and Development testing. By employing Bayesian methods, it is possible to define a prior model and update the model as test data become available. This makes it possible to build and interrogate the model earlier in the test programme. The model can then be used to identify the best settings and plan further test work.Examples are given to illustrate ways for collecting and using the required engineering knowledge, alternative criteria for selecting the test points and some Bayesian analysis tools.