Summary: By combining a constrained Kiefer-Wolfowitz search with an automatic learning algorithm, it is shown that asymptotically normal convergence of an estimator to a global optimum under reasonably lenient assumptions can be attained. It is enough that the objective function be smooth and locally strictly convex at its minima. The central conclusion is that if \(\theta_ n\) is the estimate produced by the method shown at the \(n\)th decision epoch, then for some global minimizer \(\theta^*\), \(n^{1/3}(\theta_ n-\theta^*)\) is asymptotically normally distributed. This coincides with the conventional Kiefer-Wolfowitz convergence rate to a local optimum. Whereas this study was motivated by needs of machine learning, the basic plan would seem applicable to root- finding tasks, and to other types of stochastic approximation algorithms.