This paper presents a 3D distance measurement accuracy improvement for stereo vision systems using optimization methods A Stereo Vision system is developed and tested to identify common uncertainty sources. As the optimization methods are used to train a neural network, the resulting equation can be implemented in real time stereo vision systems. Computational experiments and a comparative analysis are conducted to identify a training function with a minimal error performance for such method. The offered method provides a general purpose modelling technique, attending diverse problems that affect stereo vision systems. Finally, the proposed method is applied in the developed stereo vision system and a statistical analysis is performed to validate the obtained improvements.