Adaptive Neural Networks (ANN) can be used in the analysis of a complex panorama of interconnected input/output industrial data, even when they present substantial noise. The ANN, despite presenting substantial mathematical complexity associated with non-linear parameterization (which includes transfer equations and corresponding “training”), are largely used under industrial conditions in several engineering areas (such as in steelmaking), with substantial success. This work shows the applicability of the ANN in a specific case related to the analysis of internal defects of extruded aluminum sections (occurring both at the head and at the extrusion tail), and the associated bar hardness as a function of process parameters such as: billet temperature, extrusion ratio, ram speed and billet length. Results were analyzed in terms of the adhesion to an ANN built upon the collected industrial data, as well as the relevance of each variable within the ANN.