The problem is to solve a large, ill-conditioned linear system \(Ax=b\) of size \(n\), where \(b=\hat{b}+e\) with \(\hat{b}\) the ``true'' vector and \(e\) some error. Tikhonov regularization minimizes \(\|Ax-b\|^2+\mu^{-1}\|x\|\) with \(\mu\) a regularization parameter. The proposed (range restricted) Arnoldi-Tikhonov regularization looks for the minimizer \(x_{\mu,\ell}\) in the Krylov subspace \(K_\ell(A,Ab)=\text{span}\{Ab,A^2b,\dots,A^{\ell}b\}\) produced by the Arnoldi method. It is shown that, under some conditions, \(\varphi_\ell(\mu)=\|Ax_{\mu,\ell}-b\|^2\) is convex with a unique minimum, and \(\ell\) is taken to be the smallest (or slightly larger) index for which \(\varphi(\mu)1\) reflects the uncertainty of the estimate \(\varepsilon\). An efficient implementation is described that compares favorably with range restricted generalized minimal residual (GMRES) method (GMRES applied within \(K_\ell(A,Ab)\)), and other regularization methods of the authors which is illustrated by several numerical examples.