Abstract: Robust control techniques have allowed engineers to create more descriptive models through the inclusion of uncertainty in the form of both plant perturbations and additive noise. This additional information allows for the creation of models which are robust to any deviations from the physical system, provided the uncertainties properly define these differences. Model validation techniques were developed to answer the question: Given an experimental data set and a model with plant perturbations and additive noise, could the model reproduce the observed input-output data? As a result, model validation provides a guarantee that the uncertain model is able to account for all experimental data. In addition to traditional robust control applications, model validation has seen applications in other areas, particularly in the field of structural health monitoring. This presentation will provide an overview of the current state of the art for model validation in the context of robust control, as well as a few illustrating examples and possible directions for future research.