A number of tasks in airborne laserscanning require the establishment of correspondences between point data from neighbouring strips, or referencing between point clouds and object models. These tasks may be solved by interpolating laserscanner data, which are usually irregularly distributed 2¹/₂-D points, to a regular grid and applying standard photogrammetric matching techniques. Instead, the paper presents a formulation of least squares matching based on the original data points in a triangulated irregular network structure, thus avoiding degrading effects caused by the interpolation. The technique determines shifts in all three coordinate directions together with their covariance matrix. It can be shown that applying matching techniques to laserscanner data causes large systematic errors of the shift parameters in the case of partial occlusions. The presented formulation on the basis of a TIN structure allows for manifold extensions to solve this problem. The technique and a number of extensions have been implemented and applied to the measurement of strip errors in an airborne laser scanner dataset with moderate point density, consisting of 20 strips including crossing strips. The paper shows the results from this test, discusses the advantages of the presented technique and the limitations of matching techniques applied to laserscanner data. Special attention has to be paid to problems caused by height discontinuities in the data and by the fact that the design matrix in least squares matching is derived from observations with stochastic properties. The latter leads to precision figures that are usually much too optimistic. A detailed analysis of the design matrix and extensive testing lead to better funded precision figures for the standard deviation of the obtained shift parameters. These are in the order of one centimeter in height direction and one decimeter in horizontal direction, corresponding to about ¹/₂₀th of the average point spacing.