An automatic adaptation procedure based on a neural network etalon model of a scanning tunnelling microscopy system is proposed in this paper. The behaviour of the adaptive system applied to different sample surfaces and scan ranges is investigated. An improvement of the system stability and quality of the scan images is obtained.