Development of performant state estimators for industrial processes like copper extraction is a hard and relevant task because of the difficulties to directly measure those variables on-line. In this paper a comparison between a dynamic NARX-type neural network model and a support vector machine (SVM) model with external recurrences for estimating the filling level of the mill for a semiautogenous ore grinding process is performed. The results show the advantages of SVM modeling, especially concerning Model Predictive Output estimations of the state variable (MSE < 1.0), which would favor its application to industrial scale processes.