A hardware friendly stereo matching algorithm by employing a weighted support region was proposed with considering the local similarity and proximity of the local region to acquire the depth information in the real scenes accurately. Firstly, the input image were filtered to remove the noise and the results of the Mini-Census transform were used to calculate the Hamming distance. Then, the weighted support region was built to finish the cost aggregation and furthermore to find the minimal matching cost and generate a raw disparity map by the winner-take-all method. Finally, the raw disparity map was refined to generate the optimal disparity map and to deduce its depth distribution in the real scenes. The experimental result shows that the algorithm produces the disparity map accurately in different illumination conditions and different scenes, and its processing average bad pixel rate is only 6.77% when the images of the standard database are processed by the proposed algorithm. The proposed algorithm reduces computational complexity and is characterized by higher accuracy, good adaptability and fine robustness. It provides a basis for design and implementation of specific hardware for high accuracy stereo matching.