This paper presents an adaptive iterative learning control(AILC) scheme for a class of nonlinear systems with unknown time-varying delays and unknown input dead-zone.A novel nonlinear form of dead-zone nonlinearity is presented.The assumption of identical initial condition for iterative learning control(ILC) is removed by introducing boundary layer function.The uncertainties with time-varying delays are compensated for by using appropriate Lyapunov-Krasovskii functional and Young’s inequality.Radial basis function neural networks are used to model the time-varying uncertainties.The hyperbolic tangent function is employed to avoid the problem of singularity.According to the property of hyperbolic tangent function,the system output is proved to converge to a small neighborhood of the desired trajectory by constructing Lyapunov-like composite energy function(CEF) in two cases,while keeping all the closedloop signals bounded.Finally,a simulation example is presented to verify the effectiveness of the proposed approach.