This paper describes the application of a collection of data mining methods to solve a calibration problem in a quantitative chemistry environment. Experimental data obtained from reactions which involve known concentrations of two or more components are used to calibrate a model that, later, will be used to predict the (unknown) concentrations of those components in a new reaction. This problem can be seen as a selection + prediction one, where the goal is to obtain good values for the variables to predict while minimizing the number of the input variables needed, taking a small subset of really significant ones. Initial approaches to the problem were principal components analysis and filtering combined with two prediction techniques: artificial neural networks and partial least squares regression. Finally, a parallel estimation of distribution algorithm was used to reduce the number of variables to be used for prediction, yielding the best models for all the considered problems.