This paper describes the development of algorithms for moment-based stereopsis as the feature descriptors and stereo matching algorithms. Moment functions capture global characteristics of an image shape and are ideally suited for obtaining the optimal matching positions of small windowed regions in a stereo image pair. Among the class of moment functions, discrete orthogonal moments do not exhibit large dynamic range variations, are robust with respect to image noise, and have superior feature representation capabilities. These considerations have led to the choice of using Scaled Tchebichef Moments as feature descriptors for stereo analysis in this research. The journal also compares the stereo matching performance of conventional methods such as the cooperative stereopsis, correlation and window-based matching techniques, with Geometric and Tchebichef Moments. Extensive analysis using various types of images (synthetic and real, binary and gray-level) was carried out with interesting results. A suitably chosen moment vector (known as Scaled Tchebichef Moments) together with dynamic programming yielded highly satisfactory results in a stereo matching algorithm.