An important and challenging problem is the determination of appropriate control structures that minimize the loss of process performance under the effect of uncertainties. This can be achieved by selecting subsets of controlled and manipulated variables and designing their interconnection. In this paper, a systematic optimization methodology for the Control Structure Selection Problem (CSSP) is presented. The proposed formulation (a) improves the accuracy of calculations and (b) reduces computational time and effort necessary. Specifically, the error involved in the approximation of the nonlinear constraint that defines the magnitude of the back-off vector is reduced by the introduction of a more accurate linear approximation. In addition, the methodology is able to handle simultaneously occurring disturbances and estimate their worst impact on process economics. The computational time is significantly reduced by eliminating the state variables from the final formulation. Two case studies are presented to demonstrate the benefits of the proposed algorithm. (C) 2013 Elsevier Ltd. All rights reserved.