A credit scoring classification problem can be defined as a decision process in which information from application forms for new or extended credit is used to separate the applicants into good and bad credit risks. In the credit industry, it is important to find a method that optimally separates applicants into ‘goods’ and ‘bads’ as good classification models can provide competitive advantage. These classification models can be developed by statistical techniques (e.g. statistical discriminant analysis and logistic regression), neural networks and mathematical programming (MP) discriminant analysis methods, although MP methods are less widely used in practice in spite of their advantages, e.g. MP methods are non-parametric and desired classifier characteristics can be represented by constraints in the MP model. In this paper, a MP model is described and compared with other known methods, using real data. The MP model uses minimization of the sum of the deviations of misclassified observations from the discriminant function as its objective function. The performance of this MP model is evaluated on three datasets for credit card applications and is compared with the performance of ak-NN classifier, discriminant analysis, support vector machines and and logistic regression.