In this paper a new algorithm for robustness analysis of uncertain parametric systems is proposed. The algorithm adopts a probabilistic approach to find a multidimensional region in the uncertainty parameter space where a system satisfies a given property. In particular it finds a (suboptimal) maximum volume hyper-rectangle in which the given system’s property is satisfied with a pre-assigned confidence. The algorithm has been applied for the robustness analysis of the Italian Aerospace Research Centre’s unmanned space vehicle demonstrator’s maneuverability. The use of the algorithm during the design phase of the project lowered the effort spent in aerodynamic wind tunnel testing on specific coefficients that did not require the reduction of the uncertainty ranges. Moreover the algorithm has been used for estimating the flying test bed’s initial state displacement compatible with a safe mission execution to support online launch decisions. Effectiveness of the proposed method in terms of computational efficiency and reliability has been demonstrated by comparing the results with a deterministic method that finds the actual region in the uncertainty space where the system properties are verified. The proposed algorithm allows the computational burden of robustness analysis to be drastically reduced, particularly when the number of uncertain parameters is greater than three.