This paper is concerned with the problem of detecting and measuring cylindrical objects in range data. Two different approaches are developed for the detection phase. The first approach involves a sequential process of segmenting and classifying every potential surfaces. The classification is based on the decomposition of a quadratic surface model. The second approach utilizes the Hough transform to detect the cylindrical pixels. The transform may produce significant clusters in the parameter space. By performing least square fit to those detected pixels, one can derive useful information about sizes and locations of the cylindrical objects. Synthetic range images are generated to test these ideas.