A novel fast adaptive tracking scheme based on stochastic neural network for multi-maneuvering targets is presented. The properties of the joint probabilistic da-ta association (JPDA) are analysed, the data association is reduced to be a sort of con-straint combinatorial optimization problem, and the computational burden of multi-maneuvering target tracking (MMTT) can be decreased drastically by stochastic neural network. Computer simulations show that the scheme proposed has high convergence performance, a good tracking accuracy and robustness to the uncertainty of maneuvering targets and clutter environments.