In this paper a real-time object recognition system is realized, based on the Scale Invariant Feature Transform (SIFT) algorithm. The system mainly contains a display, a camera and an image acquisition and processing board developed by our research team. An FPGA chip and a DSP chip are embedded in the card as the major calculation units, which make real-time computation possible. The whole recognition algorithm is divided into three parts: the detection of SIFT keypoints, the extraction of SIFT descriptors and the final object recognition. In order to achieve real-time detection of SIFT keypoints through hardware computation on FPGA, the original SIFT algorithm is adapted to accommodate the parallel computation and pipelined structure of hardware. Using a mode of DSP invoking a customized FPGA module, a 72-dimensional keypoint descriptor is proposed to save memory space and to cut down the computing cost in keypoints matching. The recognition proceeds by matching individual features to a database of features from known objects using a fast approximate nearestneighbor search algorithm changed based on the k-d tree and the BBF algorithm. In addition, three matching strategies are adopted to discard the false matches so as to improve the accuracy of recognition. The object recognition functionality is mainly achieved in the DSP. A model database is built and used to test the accuracy and effectiveness of the system.