In this work, a neural network approach is applied to multiobjective optimization prob- lems in order to expand the set of optimal solutions. The network is trained using results obtained from existing evolutionary multiobjective optimization approaches. The network is then evaluated based on its performance against those same approaches when given more processing time. The results are collected from a set of well-known benchmark mul- tiobjective problems, and its performance is evaluated using various indicators from the multiobjective optimization literature. Preliminary experiments reveal the viability of this approach for expanding the set of solutions to multiobjective problems. Further experiments prove that it is possible to train the neural network in a reasonable time using heuristic methods. The results of this training approach are shown to be very competitive with the underlying evolutionary mul- tiobjective optimization approach that was used to produce the training set. Additional experiments reveal the applicability of this approach across existing multiobjective opti- mization approaches.