In recent years, soft sensors have been established as a valuable alternative to the traditional hardware sensors for the acquisition of critical information regarding ``difficult-to-measure'' process variables and/or parameters in chemical process monitoring and control. Soft sensors can also be modified as a novel process identification tool for process monitoring and model based control. Often, in polymer industries the main polymerization reaction is highly nonlinear and complex to model accurately by the conventional ``first principles'' approach. In such cases, genetic programming (GP)-a novel artificial intelligence-based exclusively data driven modeling technique-can be employed for process identification. In this work GP-based soft sensors have been developed for a continuous styrene polymerization reactor. The resulting GP-based models (soft sensor) showed high prediction and generalization performances. The best performing model was successfully utilized in designing a model predictive control (MPC) scheme for the polymerization reactor.