The challenge in building disparity maps is to determine the best method to calculate consistency among points and use it to approximate the differences between the views. Fortunately, the number of methods to construct disparity maps have increased in the last years. On the other hand, a lot of work needs to be done to evaluate these methods by using different arguments. Besides, it is not clear enough how to build stereo vision algorithms with concepts as reuse and scalability. Thus, we propose a software design architecture to be applied in stereo vision systems. Furthermore, we have implemented disparity methods to perform an evaluation which objective was to determine what cost function better fits in each method analyzed. We conclude that MLMH method with SSD cost function is a good choice to be applied in the scenes we have selected, according to the statistical analysis performed.