Airborne laser scanning has become a standard method for recording topographic data. A new generation of laser scanners digitises the complete waveform of the backscattered signal and thus offers the possibility of analysing the signal shape. As a product of the laser scanning, a digital surface model (DSM) or a digital terrain model (DTM) can be derived. In water regions, data acquisition by laser scanning is limited to the water surface because the near-infrared laser pulses hardly penetrate water. Therefore, a height model generated from laser scanner point clouds over water regions does not represent the actual terrain. The generation of a DTM thus requires the detection of water surfaces. In this study, a method for the detection and classification of water surfaces in airborne laser scanning data is proposed. The method works with both geometrical features (e.g. height or height variation) and characteristics of the pulses derived from the full waveform of the returned signal (e.g. intensity or pulse width). In our strategy, based on fuzzy logic, all classification parameters are derived automatically from training areas. According to their statistical distributions, the features are considered with individual weights. The aim of this paper is to analyse crucial features for classification and to investigate the potential of full waveform laser scanning data for this application. We present results from different areas with lakes and rivers, analysing the contribution of the individual groups of features for the detection of water surfaces.