Re-vamping of industrial turbo-machinery is commonplace in the oil and gas industry in applications where subterranean combustion is used for oil extraction. The current case study refers to such an industrial compressor re-vamping, using a state of the art 3D fully viscous CFD methodology coupled with artificial neural networks (ANNs) and genetic algorithms (GA). The ANN is used to establish correlations within a database of CFD simulations of geometrical variations of the original rotor and the GA uses those correlations to estimate an optimum. The estimate is then tested with the same CFD method and the results are fed back into the database, increasing the accuracy of the ANN correlations. The process is reiterated until the optimum estimated by the GA is confirmed with the CFD simulations. The resulting geometry is superior to the original in terms of efficiency and pressure ratio as well as the range of stabile operation, as confirmed by the successful implementation in the field. In this paper we present an analysis of why the optimized geometry achieves superior performances to the original one. Further work will present comparison between the detailed experimental data and CFD.