It has been challenging to recognize walking humans at arbitrary poses from a single or small number of video cameras. Attempts have been made mostly using a 2D image/silhouette-based representation and a limited use of 3D kinematic model-based approaches. In this paper, the problem of recognizing walking humans at arbitrary poses is addressed. Unlike all the previous work in computer vision and pattern recognition the models of walking humans are built using the sensed 3D range data at selected poses without any markers. An instance of a walking individual at a different pose is recognized using the 3D range data at that pose. Both modeling and recognition of an individual are done using the dense 3D range data. The proposed approach first measures 3D human body data that consists of the representative poses during a gait cycle. Next, a 3D human body model is fitted to the body data using an approach that overcomes the inherent gaps in the data and estimates the body pose with high accuracy. A gait sequence is synthesized by interpolation of joint positions and their movements from the fitted body models. Both dynamic and static gait features are obtained which are used to define a similarity measure for an individual recognition in the database. The experimental results show high recognition rates using our range based 3D gait database.