A novel Multi-sensor Order Statistic Unscented Probabilistic Data Association Algorithm (MSOSUPDA) is proposed for the multi-sensor multi-target tracking problem of nonlinear system in clutter. In the new algorithm, the problem of interest is first translated into multiple nonlinear single-sensor multi-target tracking problems. Then, the association of measurements of single sensor to multiple tracks is implemented according to the principle of Order Statistics Probabilistic Data Association (OSPDA). Based on these, Unscented Kalman Filter (UKF) is used for the propagation of state distribution in nonlinear system and the MSOSUPDA is derived. Compared with the MSJPDA/EKF, the accuracy and robustness of MSOSUPDA are improved. Furthermore, computation complexity of the proposed algorithm is decreased obviously on account of the use of OSPDA. According to the simulation results, the divergence ratio and the processing time of our proposed algorithm are equal to 19 and 70 percent of the MSJPDA/EKF algorithm respectively.