To　identify　Hammerstein　systems,　a　variety　of　Hammerstein　filters　have　been　proposed.　However,　most　of　them　assume　the　nonlinear　part　in　Hammerstein　systems　to　be　polynomial　in　the　process　of　modeling,　which　restricts　their　applicability　in　many　practical　situations.　In　this　paper,　a　simple　kernel　adaptive　filter　(KAF)　called　kernel　least　mean　square　(KLMS)　combined　with　coherence　criterion　(CC)　is　used　to　approximate　the　nonlinear　part　of　a　Hammerstein　system,　resulting　in　the　kernel　adaptive　Hammerstein　filter　(KAHF).　The　KAHF　can　identify　various　Hammerstein　systems　well　without　any　prior　knowledge　of　nonlinear　part.　Simulation　results　confirm　the　desirable　performance　of　the　new　method.