We consider the application of sequential Monte Carlo (SMC) methodology to the problem of joint mobility tracking and handoff detection in cellular wireless communication networks. Both mobility tracking and handoff detection are based on the measurements of pilot signal strengths from certain base stations. The dynamics of the system under consideration are described by a nonlinear state-space model. Mobility tracking involves an online estimation of the location and velocity of the mobile, whereas handoff detection involves an online prediction of the pilot signal strength at some future time instants. The optimal solutions to both problems are prohibitively complex due to the nonlinear nature of the system. The SMC methods are therefore employed to track the probabilistic dynamics of the system and to make the corresponding estimates and predictions. Both hard handoff and soft handoff are considered and three novel locally optimal (LO) handoff schemes are developed based on different criteria. It is seen that under the SMC framework, optimal mobility tracking and handoff detection can be implemented naturally in a joint fashion, and significant improvement is achieved over existing methods, in terms of both the tracking accuracy and the trade-off between service quality and resource utilization during handoff.