In view of the high computational complexity of the existing joint target tracking and classification (JTC) algorithmwhich has neither closed form nor modular structurewe united the models of all predicted target types to form a global multimodel set.Thenwe proposed a joint target tracking and classification algorithm based on global multiplemodel(GMMJTC) by applying Bayes rule to the target state probability density function and target class probability mass function simultaneously under the assumption that the kinematic and attribute measurement processes are conditional independent.The GMMJTC algorithmwhich consists of a Kalman global multiplemodel filter and a Bayesian classifierhas a closed form with a modularized structuretogether with a lower computational complexity.Its more suitable for realtime applications.The simulation results confirm the effectiveness of the proposed GMMJTC algorithm.