The present work consisted on the characterization of the spatial distribution of hydraulic parameters on the Almádena-Odeáxere aquifer system (AO) using the automatic calibration of a finite-element numeric model, in order to improve the simulation accuracy of the aquifer’s hydraulic behaviour. This development has its foundations based on model variants already implemented at the University of Algarve to investigate the hydraulic properties of the AO on the framework of the regional scale groundwater flow studies concerning Algarve aquifers. The state-of-the-art of the aquifer’s hydrogeology was based on previous investigations, taking place on the last years in Algarve, but also on recent fieldwork, namely on the collection of field data from a monitoring network, designed in articulation with the “POCTI/AMB/57432/2004” investigation project, which provided the feedback information needed for the improvement of model variants developed during the course of the present work. Instead of using a classic, time consuming, trial and error approach for the purpose of determining hydraulic parameters controlling groundwater flow at AO, an automatic inverse calibration algorithm was used, allowing the achievement of parameter distribution values capable of generating realistic hydraulic flow simulations. The Gauss-Marquardt-Levenberg method of nonlinear parameter estimation, available at the PEST algorithm was assembled to the finite element flow model, which is based in the use of the Galerkin method of weighted residuals. The results obtained by the use of the inverse method have revealed a good fit between simulated and measured head values, since the correlation coefficient, R, value was higher than 0,9 (0,9967) and the sum-of-squared weighted residuals between model outcomes and corresponding field data (i.e. the objective function, F) was only 4,56 m. The obtained spatial distribution of transmissivity, ranging from 86 m2/day to 8158 m2/day on 16 zones, allowed a step further on the reliability of future simulations of spatial distribution and temporal evolution of state variables in natural conditions and considering different scenarios of water use.