Resumo. Fisher Discriminant Analysis (DA) seeks a linear combination of independent variables maximizing separation of predicted groups and also permits new observations for being classified in groups know a priori. We applied DA with eight structural attributes of vegetation obtained of systematic tree inventory surveys realized in five physiognomies types in the Brazilian Pampa biome. Later, 10 new samples were randomly selected from the same vegetation types to perform model validation. The DA generated four discriminant functions (DFs), where the first two had 88.4% power for discriminating groups (DF1 = 74.4% and DF2 = 14%). From the structural attributes used in the model, species richness, commercial height, and total height were related to DF1. Basal area and maximum stem diameter were related to DF2. The others DFs and structural variables have had less power of discriminating the groups. The DA classified 100% of the cases in their respective groups, showing a high efficiency of the chosen discriminating variables. The new forest samples inserted in the model were also classified with a small degree of error. The use of DA models should be enhanced because it is simple and more effective to express a forest classification model than the other descriptive multivariate methods.