Iterated Tikhonov regularization for the solution of nonlinear ill-posed problems is investigated. In the case of linear ill-posed problems it is well-known that (under appropriate assumptions) the \(n\)-th iterated regularized solutions can converge like \(O(\delta^{{2n \over 2n+1}})\), where \(\delta\) denotes the noise level of the data perturbation. Conditions are given that guarantee this convergence rate also for nonlinear ill-posed problems, and these conditions are motivated by the mapping degree. The results are derived by a comparison of the iterated regularized solutions of the nonlinear problem with the iterated regularized solutions of its linearization. Numerical examples are presented.