Melt index is the most important parameter in determining the polypropylene grade. Since the lack of proper on-line instruments, its measurement interval and delay are both very long. This makes the quality control quite difficult. A modeling approach based on stacked neural networks is proposed to estimation the polypropylene melt index. Single neural network model generalization capability can be significantly improved by using stacked neural networks model. Proper determination of the stacking weights is essential for good stacked neural networks model performance, so determination of appropriate weights for combining individual networks using the criteria about minimization of sum of absolute prediction error is proposed. Application to real industrial data demonstrates that the polypropylene melt index can be successfully estimated using stacked neural networks. The results obtained demonstrate significant improvements in model accuracy, as a result of using stacked neural networks model, compared to using single neural network model.