The purpose of this talk is to illustrate the use of discriminant analysis based on tree-structured graphical models for discrete variables. This is done by comparing its empirical performance through estimated error rates for real and simulated data. The results show that discriminant analysis based on tree-structured graphical models is a simple nonlinear method competitive with, and sometimes superior to, other well-known linear methods like those assuming mutual independence between variables and linear logistic regression.