A complex sequence of tests on components and the system is a part of many manufacturing processes. Statistical imperfect test and repair models can be used to derive the properties of such test sequences but require model parameters to be specified. We describe a technique for estimating such parameters from typical data that are available from past testing. A Gaussian mixture model is used to illustrate the approach and as a model that can represent the wide variety of statistical properties of test data, including outliers, multimodality and skewness. Model fitting was carried out using a Bayesian approach, implemented by MCMC. Copyright © 2011 John Wiley & Sons, Ltd.