Optimal and feasible operation of process plants demands accurate knowledge of the effect of parameter uncertainty on process design and operation. There has been considerable effort toward accurate representation of the feasible operation range, and different metrics have been proposed in the literature to quantify the operational flexibility. While these methods are largely successful in addressing convex problems, their applicability becomes restricted for general nonconvex problems. The feasibility analysis technique proposed in this paper considers the feasible region as an object and applies surface reconstruction ideas to capture and define the shape of the object. The procedure starts by first sampling the feasible region to have a representation of the feasible space and then constructing an a shape with the sampled points, thus generating a polygonal representation of the feasible parameter space. Finally, any point can be checked for its feasibility by applying the point-in-polygon algorithm. This method is general and can be applied to any convex, nonconvex, or even disjoint problems without any further modifications.