Effective self-organization schemes lead to the creation of autonomous and reliable robot teams that can outperform a single, sophisticated robot on several tasks. We present here a novel, vision-based microscopic framework for active and distributed object-recognition and pose-estimation using a team of robots of simple construction. The team performs the task of locating a given object(s) in an unknown territory, recognizing it with sufficient confidence and estimating its pose. The larger goal is to experiment with probabilistic frameworks and graph-theoretic methods in the design of robot teams to achieve autonomous self-organization independent of the task at hand. We have chosen 3D object recognition as a first problem area to evaluate the effectiveness of our system design. The system comprises a probabilistic framework for the successful detection of the object in a coordinated manner and adaptive measures in case of machinery failures or presence of obstacles. A pose estimation method for the detected object and graph theoretic solutions for optimal field coverage by the robots are also presented. Each robot is provided with a part-based, spatial model of the object. The object to be recognized is taken to be much bigger than the robots and need not fit completely into the field of view of the robot cameras. We assume no knowledge of the internal parameters of the robot cameras and perform no camera calibration procedures. Initial simulation results corroborate our system design and field coverage methods. © 2006 IEEE.