African mahogany has noble wood of high economic potential and extensive use in the furniture industry, civil construction and production of panels and laminates presenting good workability. In order to estimate the volume of individual trees in an African mahogany (Khaya invorensis) stand in the municipality of Pirapora, Minas Gerais, Brazil, the objective of this study was to evaluate the use of Artificial Neural Networks (ANNs). Throughout the NeuroForest 4.0 software, 2400 networks were trained, with 60% separation of data for training and 40% for validation, with two different configurations of input signals, the first one using total height (HT) and the diameter at 1.3 m height (DAP); and the second using diameters at four different tree heights. The (ANNs) trained with (HT) and (DAP) obtained the best square root mean squared error (RMSE) statistical results, correlation between estimated and expected values and residual distribution. Thus, the use of Artificial Neural Networks in the estimation of volume with bark of (Khaya ivorensis) is efficient.