© 2017 Taylor & Francis Group, London. Complex engineering systems are usually investigated by running expensive computer models. However, high-dimensional input and computational cost often hinder the calibration of such models. History matching is a calibration strategy, which has been successfully applied in many disciplines. It starts with a specific observation of a physical system, then sequentially discards the implausible input set, that is, the range of input values that do not provide a good fit between the model output and the available data. The computational efficiency depends on Bayesian emulation, which provides a statistical approximation to the model output. At each iteration, the non-implausible domain is sampled to run the simulator until it finds the matching input domain. Because the non-implausible domain can be significantly smaller than the original space, this paper proposes to use an engineering reliability method: Subset Simulation. This strategy uses Markov Chain Monte Carlo to sample from a failure domain. Numerical examples are provided to show the potential of this sampling scheme for model calibration.