Abstract: Aiming at the problem of slow speed and noise-sensibility in image matching，the paper suggests a GPSO approach to image matching，which is based on grey theory and particle swarm optimization.Firstly，several matching positions and updating speeds are acquired by the initialization of the particle swarm.Secondly，a referential sequence and a comparative sequence are separately constructed by the histogram information of the template image and the current searching subimage.Thirdly，based on the grey relational degree between the two sequences，a fitness function is designed.And then，guided by the personal experience and the global experience，all of the particles concurrently approach to the best matching position generation by generation.The experimental results indicate that the algorithm not only obtains the precise position，but also obviously increases the matching speed and the robustness.