A general central limit theorem is proved for estimators defined by minimization of the length of a vector-valued, random criterion function. No smoothness assumptions are imposed on the criterion function, in order that the results might apply to a broad class of simulation estimators. Complete analyses of two simulation estimators, one introduced by Pakes and the other by McFadden, illustrate the application of the general theorems.