In this paper, we propose a visual servo strategy scheme for robot soccer systems. All the controls of androids are based on the image processing of top-view screens captured by an overhead CCD camera upon the game field. To separate the targets from the background, a self-organizing map neural network (SOMNN)-based image processing scheme is proposed, in which the gravity of every color is calculated using the algorithms of image filtering, division, and morphology. It provides the coordinates and heading angle of every soccer android using the information of color pitches. The decision-making subsystem adopts the A* algorithm to determine a collision-free, direct, and optimal path so that the androids can trace a ball faster and kick well. The experimental results in the real FIRA AndroSot games demonstrate the feasibility of the proposed visual control system.