Many photogrammetric and GIS applications, such as city modelling, change detection and object recognition, deal with surfaces. Change detection involves looking for differences between two surface models that are obtained from different sensors, for example an optical sensor and a laser scanner, or by the same sensor at different epochs. Surfaces obtained through a sampling process may also have to be compared for future processing (for example transformation parameter estimation and change detection). Surface matching is therefore an essential task in these applications. The matching of surfaces involves two steps. The first step deals with finding the correspondences between two surface points and/or patches. The second step requires the determination of transformation parameters between the two surfaces. However, since most surfaces consist of randomly distributed discrete points and may have different reference systems, finding the correspondences cannot be achieved without knowing the transformation parameters between the two surfaces. Conversely, deriving the transformation parameters requires the knowledge of the correspondence between the two point sets. The suggested approach for surface matching deals with randomly distributed data sets without the need for error prone interpolation and requires no point-to-point correspondence between the two surfaces under consideration. This research simultaneously solves for the correspondence and the transformation parameters using a Modified Iterated Hough Transform for robust parameter estimation. Several experiments are conducted to prove the feasibility and the robustness of the suggested approach, even when a high percentage of change exists.