Stereo vision cameras are widely used for finding a path for obstacle avoidance in autonomous mobile robots. The Scale Invariant Feature Transform (SIFT) algorithm proposed by Lowe is used to extract distinctive invariant features from images. While it has been successfully applied to a variety of computer vision problems based on feature matching including machine vision, object recognition, image retrieval, and many others, this algorithm has high complexity and long computational time. In order to reduce the computation time, this paper proposes a SIFT improvement technique based on a Self-Organizing Map (SOM) to perform the matching procedure more efficiently for feature matching problems. Matching for multi-dimension SIFT features is implemented with a self-organizing map that introduces competitive learning for matching features. Experimental results on real stereo images show that the proposed algorithm performs feature group matching with lower computation time than the SIFT algorithm proposed by Lowe. We performed experiments on various set of stereo images under dynamic environment with different camera viewpoints that is based on rotation and illumination conditions. The numbers of matched features were increased to double as compared to the algorithm developed by Lowe. The results showing improvement over the SIFT proposed by Lowe are validated through matching examples between different pairs of stereo images. The proposed algorithm can be applied to stereo vision based robot navigation for obstacle avoidance, as well as many other feature matching and computer vision applications.