The work aims to propose a new method for the recognition of aluminium can cover image based on the genetic algorithm, so as to realize the automation of laser code printing at the back of aluminium can cover pull ring. A set of automatic laser code printing machine for aluminium can cover was firstly set up. Based on the setup experiment system, images of aluminium can cover were captured by CCD in real time. Then, the median filter and gray enhancement operations for the captured images were performed. On this basis, a new threshold segmentation method for the images of can cover based on the genetic algorithm was researched. Key parameters, such as the total number of individuals, the crossover rate and the variation rate were analyzed and determined. Therefore, the binary image of aluminium can cover was obtained, and the errors of the algorithm processing result were analyzed at the same time. After 15 times' iterative computations, the genetic algorithm could be convergent and the optimal image threshold was obtained. The running time of the whole algorithm was about 30 ms, and the final image accuracy was about 7.9 pixel. The image threshold segmentation based on genetic algorithm has good real-time performance and the segmented image has high accuracy. Compared with the traditional Ostu threshold segmentation method, the image threshold segmentation method can obtain more information and restrain the image interference produced by the asymmetry of brightness. In the meantime, the sobel edge detection is performed on the images subject to threshold segmentation based on the genetic algorithm. Therefore, the clearer edge image of can cover can be acquired to lay a foundation for the accurate location of the laser code printing.