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The objective of this study was to compare different alternatives to estimate the stem volume of individual trees in four different forest formations in the Minas Gerais state, Brazil. The data were obtained in a forest inventory procedure performed by the Minas Gerais Technological Center Foundation. The stem volumes were computed by the Smalian expression up to the outside bark diameter equal to 4 cm. The volume data of outside bark, diameters (DBH) and total heights were used to fit a Schumacher and Hall equation for each forest formation, considering the structures of the linear fixed and mixed models. Next, 100 Multilayer Perceptron artificial neural networks (ANN) were trained in a supervised manner. In addition, we evaluated eight support-vector machine regression (SVMR). The criteria to evaluate the performance of all the alternatives studied were: the correlation between the observed and estimated volumes, the square root of the mean square error and the frequency distribution by percentage relative error class. After the analyzes, all the alternatives were verified to estimate the volume of the individual trees in the different forest formations. Although the alternatives presented close statistics in the validation process, the graphical analysis of the error distribution showed greater precision of the estimates of the mixed linear models for the four formations. Given the results, it is concluded that there is no absolute superiority of one alternative over the others, and that all of them should be evaluated to find the one which best describes or explains the dataset.