This paper describes a Bayesian multitarget identification algorithm for a multisensor airborne surveillance system. The identification algorithm represents a part of the joint multitarget tracking and identification algorithm derived for the airborne surveillance system. We show that the addition of identity to the position and velocity state for each target improves the capability to associate sensor reports with target tracks. This paper also formulates a generalized model for the sensor observables used for target identification: the generalized model is used to develop a recursive identification algorithm; it is also used to evaluate the amount of information provided by each of the sensor observables for target identification. Results obtained from a prototype of the decision aid demonstrate the effectiveness of the identification algorithm to identify targets in a multitarget surveillance scenario.