History matching is a model (pre-)calibration method that has been applied to computer models from a wide range of scientific disciplines. In this work we apply history matching to an individual based epidemiological model of HIV that has 96 input and 50 output parameters, a model of much larger scale than others that have been calibrated before, using this or similar methods. Apart from demonstrating that history matching can analyse models of this complexity, a central contribution of this work is that the history match is carried out using linear regression, an elementary and easier to implement statistical tool compared to the Gaussian process based emulators that have previously being used. Furthermore, we address a practical difficulty of history matching, namely, the sampling of tiny, non-implausible spaces, by introducing a sampling algorithm adjusted to the specific needs of this method. The effectiveness and simplicity of the history matching method presented here shows that it is a useful tool for the calibration of computationally expensive, high dimensional individual based models.