A new methodology is proposed to design a soft sensor for a polypropylene (PP) process with grade changeover operation. In contrast to the general polyolefin process, the PP process usually produces more than 100 different grades of products. Its reaction mechanism, based on seven catalysts, is so complex that neither mechanistic nor empirical models have been successful in describing full-scale industrial applications. The proposed methodology was developed based on the hybrid modeling of novel clustering and black-box and mechanistic models. Clustering based on critical to quality enables the soft sensor to handle the complexity of many different grades. Hybrid modeling offers good predictive power for transient behaviors as well as normal behaviors. The methodology also allows us to reduce the cost of building and updating the model. The developed soft sensor was successfully applied to a real industrial process. The accurate and reliable monitoring of the melt index in the PP process helped to significantly reduce the amount of off-specification product generation.