In　this　paper,　adaptive　neural　tracking　control　problem　is　considered　for　non-strict-feedback　high-order　nonlinear　systems　with　quantized　input　signal.　Compared　with　the　logarithmic　quantizer,　the　quantizer　introduced　in　this　paper　can　avoid　chattering　problem.　The　dynamic　surface　control　(DSC)　technique　is　introduced　to　solve　the　problem　of　＇explosion　of　complexity＇,　which　is　appeared　in　the　classic　adaptive　backstepping　control　of　high-order　nonlinear　systems.　The　structural　properties　of　radial　basis　function　neural　networks　(RBF　NNs)　are　used　to　simplify　the　design　difficulty　from　the　functions　of　whole　state　variables.　According　to　the　classic　adaptive　backstepping　technique　and　neural　network　algorithm,　an　output　tracking　controller　is　designed,　which　can　guarantee　that　all　the　signals　of　the　closed-loop　system　are　semiglobally　uniformly　bounded　and　the　output　of　the　system　can　track　the　reference　signal.　Finally,　a　numerical　example　is　presented　to　verify　the　effectiveness　of　the　proposed　method.　(C)　2021　Elsevier　B.V.　All　rights　reserved.