This study proposes a new map building method for a mobile robot operating in an environment with obstacles by fusing sensor data. Required information for a map designing is supplied by fusion of different sensor data using the sequential principal component (SPC) method. We discuss mathematical and experimental issues of the method by comparing a Bayesian method that works efficiently in map building using sensor data fusion. Application of the method for grid based map building is introduced and compatibility in mobile robot navigation is demonstrated. Experimental studies are implemented on Nomad200 mobile robot successfully.