The ability of an algorithm to accurately estimate the parameters of the geometric transformation which aligns two image profiles even in the presence of photometric dis- tortions can be considered as a basic requirement in many computer vision applications. Projective transformations constitute a general class which includes as special cases the affine, as well as the metric subclasses of transforma- tions. In this paper the applicability of a recently proposed iterative algorithm, which uses the Enhanced Correlation Coefficient as a performance criterion, in the projective im- age registration problem is investigated. The main theoret- ical results concerning the iterative algorithm and an effi- cient approximation that leads to an optimal closed form solution (per iteration) are presented. Furthermore, the performance of the iterative algorithm in the presence of nonlinear photometric distortions is compared against the leading Lucas-Kanade algorithm by performing numerous simulations. In all cases the proposed algorithm outper- forms the Lucas-Kanade algorithm in convergence speed and robustness against photometric distortions under ideal and noisy conditions.