A model validation metric for multiple response is presented under the framework of probability box that characterized by mixed aleatory and epistemic uncertainties. Area metric is mainly used to quantify the measurement of agreement between the simulation results and experimental observations under the framework of the probability box. However, the traditional area metric is not suitable for validation activities with multiple outputs when the responses are related with each other. Multi-response model validation metric considering uncertainty conditions, actually returns the quantification of the difference between joint probability distribution functions of simulation results and experimental observations. The Mahalanobis distance is introduced to convert the multi-dimensional samples into Mahalanobis distance values. Then, the cumulative probability distribution function of Mahalanobis distance is defined, and probability box of the simulation and experimental observations is obtained by double-deck Monte Carlo sampling. As a result, the validation metric for multiple responses is converted into the calculation of the area of the Mahalanobis distance. A simulation example verifies the effectiveness and stability of the proposed method, and it is applied to study the “2014 Sandia Verification and Validation Challenge” engineering problem. The research demonstrates that the proposed validation metric for multiple related responses based on the Mahalanobis distance and probability box is feasible and effective.