This paper presents the preliminary results of a data mining study of a production line involving hundreds of variables related to mechanical, chemical, electrical and magnetic processes involved in manufacturing coated glass. The study was performed using two nonlinear, nonparametric approaches, namely neural network and CART, to model the relationship between the qualities of the coating and machine readings. Furthermore, neural network sensitivity analysis and CART variable rankings were used to gain insight into the coating process. Our initial results show the promise of data mining techniques to improve the production.