An extensive simulation study was performed to examine different point-to-surface registration techniques for intraoperative registration of preoperative patient data to points collected with electrophysiologic anatomy mapping systems. Three point-to-surface registration methods were evaluated using simulated points sampled from a preoperative heart model. Downhill Simplex (DS) based method outperformed the Iterative Closest Point (ICP) method and a chamfer transform based method. One hundred simulations were performed under a variety of noise and sampling conditions. Less than four pixels root mean squared distance (RMSD) error was observed when there was a 2-pixel standard deviation Gaussian noise in the point cloud coordinates. This registration error was mainly due to the added noise in the sampled points. A near optimal registration can be achieved when 50 or more points randomly sampled on the surface are used as point samples. Reasonable registration can be achieved when 25 points are used. A motion-compensating approach to registration was evaluated in order to account for the different transformation that each anatomical structure may undergo during the procedure due to respiratory motion and other factors. A piecewise registration method, which registers different anatomical structure independently, was evaluated, and favorable results were obtained as compared to a global registration approach. Further validation is in progress to evaluate the piecewise registration using realistic dynamic phantoms and in vivo animal studies.