Abstract: We consider the problem of validating computer models that produce multivariate output, particularly when the model is computationally demanding. Our strategy builds on Gaussian process-based response-surface approximations to the output of the computer model independently constructed for each of its components. These are then combined in a statistical model involving field observations to produce a predictor of the multivariate output at untested input vectors. We illustrate the methodology in a situation where the output consists of a two-dimensional output of very irregular functions.