Today, human has achieved many developments in the field of machine vision. One of these developments is the simultaneous use of several cameras. Iired by the human visual system, researchers usually use a two cameras system called stereo system. Algorithms and methods for the stereo system, uses the images of the two cameras and the results achieved by the simultaneous processing of two images. These processes include various stages during its previous from pre-calibration of the camera to the final outcome, such as distance measurement or three-dimensional reconstruction. Stereo system encompasses a wide range of applications, such as building a three-dimensional map, the location and distance of objects, robot navigation, grasping by a robot and so. In this study, we represent an object recognition and pose estimation system based on stereo images that has many applications for mobile robots. Stereo system performs localization more accurately than mono camera system by using information of two images. In this system, we use SIFT local features. SIFT feature is useful tool for object recognition in different situations because of invariance to image transforms such as rotation, translation and scale changes. Also this feature is robust to illumination changes and occlusion of objects in images, therefor it is suitable for recognition in different illumination conditions and occlusion scenes. One of the most important steps in these systems is descriptors vector matching with the set of vectors in database to find the corresponding vector or nearest vectors. Here we are trying to add parameters to the features to speed up matching step without loss of accuracy that is very important in real-time applications. This is done by adding parameters to the original feature and the speed of matching step increase in average double the quickest method. The recognition system includes two steps: model generation and recognition. In both steps we use proposed method to speed up feature matching by suitable precision. The system has been tested in various modes and system accuracy is calculated for each mode. It makes locating objects a few millimeters of error Keywords: 1-Object recognition 2- pose estimation 3-stereo images 4-SIFT features 5-feature matching.