The estimation of volumetric mass transfer coefficient, k(L)a, in stirred tank reactors using artificial neural networks has been studied. Several operational conditions (N and V-s), properties of fluid (mu (a)) and geometrical parameters (D and T) have been taken into account. Learning sets of input-output patterns were obtained by k(L)a experimental data in stirred tank reactors of different volumes. The inclusion of prior knowledge as an approach which improves the neural network prediction has been considered. The hybrid model combining a neural network together with an empirical equation provides a better representation of the estimated parameter values. The outputs predicted by the hybrid neural network are compared with experimental data and some correlations previously proposed in the literature for tanks of different sizes.