A fuzzy system has been developed to ponder update decisions both for the trajectories and shapes estimated for targets. It is embedded in an A-SMGCS Surveillance function for airport surface, based on video data processing, in charge of the automatic detection, identification and tracking of all interesting targets (aircraft and relevant ground vehicles). The tracking system captures a sequence of images, preprocesses them to extract the moving regions (blobs), and associates the blobs to tracks to estimate the number of targets in the scenario and their parameters. The system was initially built with a set of rules derived from performance analysis, and then a procedure based on neuro-fuzzy techniques was applied to automatically obtain rules from examples. A validation of learned system shows its capability to produce appropriate decisions. Results obtained with real data in representative ground operations show the system capabilities to solve complex scenarios and improve tracking accuracy.