This paper addresses the problem of joint tracking and classification (JTC) of maneuvering targets via sequential Monte Carlo (SMC) techniques. A general framework of the problem is presented within the SMC. A SMC algorithm is developed, namely a Mixture Kalman filter (MKF), which accounts for speed and acceleration constraints. The MKF is applied to airborne targets: commercial and military aircraft. The target class is modeled as an independent random variable, which can take values over the discrete class space with equal probability. A multiple-model structure in the class space is implemented, which provides reliable classification. The performance of the proposed MKF is evaluated by simulation over typical target scenarios.