Kohenen Self Organizing Map for Clustering of Airborne Laser Scanner Data Jalal Amini University of Tehran, Iran Email: jamini@ut.ac.ir Jalal Amini Student University of Tehran Email: jamini@ut.ac.ir Sean Belshaw Email: seanb@optech.on.ca One of the most widely used clustering techniques used in geomatics problems is the k-means algorithm. One of the most important issues in the correct use of k-means is the initialization procedure that ultimately deter-mines which part of the solution space will be searched. In this paper we briefly review different initialization procedures, and propose Kohonen’s Self- Organizing Maps as the most convenient method, given the proper training parameters. Furthermore, we show that in the final stages of its training procedure the Self-Organizing Map algorithms is rigorously the same as the kmeans algorithm. Thus we propose the use of Self-Organizing Maps as possible substitutes for the more classical k-means clustering algorithms in airborne laser scanner data.