A new strategy is proposed to identify Hammerstein-type nonlinear processes composed of a nonlinear static function and a linear dynamic. subsystem.; It completely separates, the identification problem of the linear. dynamic subsystem from that of the nonlinear static function using a special test signal. Then, quite useful, advantage over previous approaches ate guaranteed: we can use existing well-established linear system identification methods and their asymptotic properties of parameter estimates a,well as excellent techniques for minimal parametrization to identify the linear dynamic subsystem of the Hammerstein, process. Also, we can identify the nonlinear static function of the Hammerstein process analytically without any iterative optimization.