This article describes the application of neural networks and hybrid models to the finishing stage of nylon-6,6 polycondensation in a twin-screw extruder reactor. A planned experiment in the industrial and in the pilot plant was employed to build the neural network and the hybrid model. The hybrid model combines information calculated from the phenomenological model with the neural network model. The comparison of experimental with calculated data shows good agreement. During two years, industrial data were collected. The comparisons of the models' prediction with these data were performed and reasonable results are achieved from the industrial point of view. These models help an increase of industrial production of about 20%.