This study aims to compare the performances of the artificial neural network, decision trees and discriminant analysis methods to classify student achievement. The study uses multilayer perceptron model to form the artificial neural network model, chi-square automatic interaction detection (CHAID) algorithm to apply the decision trees method and linear discriminant analysis. The performance of each method has been investigated in different sample sizes when classifying into different numbered subgroups. The study has revealed that the artificial neural network has the best performance in large, medium and small sample sizes when classifying into six, three and two subgroups. In the very small sample size, which has homogeneous variance-covariance matrices, the discriminant analysis performs the best, while in the very small sample size, which does not have homogeneous variance-covariance matrices, it is the discriminant analysis which performs the best when classifying into six subgroups and the artificial neural network performs the best when classifying into two and three subgroups. Considering the performances of the methods with respect to sample size, it can be concluded that as the sample size gets smaller, the performance of the decision trees method gets worse, whereas the performance of the discriminant analysis method improves. No correlation of this kind has been found with regard to the artificial network method.