Several heuristic methods have been suggested for improving the generalization capability in neural network learning, most of which are concerned with a single-objective (SO) learning tasks. In this work, we discuss generalization improvement in multi-objective learning (MO). As a case study, we investigate the generation of neural network classifiers based on the receiver operating characteristics (ROC) analysis using an evolutionary multi-objective optimization algorithm. We show on a few benchmark problems that for MO learning such as the ROC based classification, the generalization ability can be more efficiently improved within a multi-objective framework than within a single-objective one.