The system is often described as a series of blocks linked together in non-linear system identification. Such block-oriented models are built with static non-linear subsystems and linear dynamic systems. This paper deals with the parameter estimation of Hammerstein systems with piecewise non-linearities, which is a blocked-oriented model where a static non-linear blocking is followed by a linear dynamic system. The basic idea is as follows. The key term separation technique is applied initially, and then a corresponding auxiliary model is constructed. Hence, the identification problem of the system is converted to a non-linear function optimization problem over parameter space. Once again, the estimates of all the parameters are obtained by a proposed particle swarm optimization algorithm. Finally, compared with the existing methods, the simulation results confirm that the presented method is valid. Moreover, the presented method is further extended to estimate Hammerstein systems with discontinuity non-linearities.