We propose a novel incremental surface-based registration technique that employs the Unscented Kalman Filter (UKF) to register two different data sets. The method not only reports the variance of the registration parameters but also has significantly more accurate results in comparison to the Iterative Closest Points (ICP) algorithm. Furthermore, it is shown that the proposed incremental registration algorithm is less sensitive to the initial alignment of the data sets than the ICP algorithm. We have validated the method by registering bone surfaces extracted from a set of 3D ultrasound images to the corresponding surface points gathered from the Computed Tomography (CT) data.