In recent years, Vision Systems have found their ways into many applications.This includes fields such as computer graphics, medical, industries such asassembly line inspection and object manipulation. The application of ComputerVision technology to factory automation, Machine Vision, is growing at rapid rate.However in most Machine Vision systems an algorithm is needed to infer 3Dinformation regarding the objects in the field of view. In this thesis presents an updated Stereo Matching Algorithm of MSOM(Modify Self Organized Maps). This technique based on artificial neural network.Using the learning rule to similarity and differentiation of both sides of the eyes tofind the most overlapping position of the images, give the best clarity for humanvisualization of 3D images, in the machine procedure on stereo vision problems.Feature selection and extraction is an important step for disparity plan. Throughstudying of several feature detector method found that image gradient filter hasdirection and can calculate the horizontal, vertical and diagonal direction of theimage and provides enough information regarding the critical points that an objectcan be characterized. In original MSOM algorithm estimating the disparity and shown the result ingray scale cannot see the distance of the objects. In this method we used aself-adapting dissimilarity measure for extract a disparity plane. Then estimatingdisparity by update the original MSOM to calculate the disparity and also indicatethe depth of the objects in the image. Red color represents nearest object thenGreen and Blue represent to furthest object. Keyword:binocular disparity; stereo-pai; modify self-organizing map (MSOM);stereo matching; self-adapting dissimilarity measure