Multiobjective optimization of chemical processes, has received considerable attention in recent years. It was particularly boosted by the availability of efficient population-based evolutionary algorithms, which have a clear advantage for generating well-distributed Pareto optimal solutions over classical methods. The main focus of these studies has been finding the Pareto optimal solutions, and providing qualitative explanation of the roles and results of the decision variables. Although this is the primary job of an optimization study, the roles played by the decision variables do not get sufficient attention, which they deserve. While the optimal Pareto provides an idea of the best trade-offs between the objectives, the solutions (decision variables) are the means to achieve it. The present study focuses on the situations where multiple solution sets are available to achieve the same or similar objective trade-off. Here, the job does not end with finding the optimal Pareto set and the best objective trade-off from it, but also includes finding the best set of variable solutions to achieve it. (c) 2006 Elsevier Ltd. All rights reserved.