This paper presents a novel approach to model and predict cutting tool wear using statistical signal analysis, pattern recognition and sensor fusion. The data are acquired from two sources: an acoustic emission sensor (AE) and a tool post dynamometer. The pattern recognition used here is based on two methods: Artificial Neural Networks (ANN), and Polynomial Classifiers (PC). In this work we compare between cutting tool wear predicted by neural network (ANN) and polynomial classifiers (PC). For the case study presented; PC proved to significantly reduce the required training time compared to that required by an ANN without compromising the prediction accuracy. The predicted results compared well to the measured tool wear. ©2008 IEEE.