Finger-spelling recognition and hand-shape recognition are two examples of real-world, multi-class recognition problems consisting of 26 and 78 classes respectively. While it is theoretically possible to solve any multi-class problem with a single "smart" classifier, the complexity of such a classifier is usually prohibitively high. This paper looks at several approaches to solving a numerous multi-class recognition problem and discusses in detail a method involving coded output. Experiments are conducted using bio-mechanical data from a human hand as input, but work is continuing concerning the extraction or this data from multi-view hand images alone. Code generation is discussed and results are presented for several different coded output cases including the Hamming, Golay, and several hybrid codes. Conclusions show that the recognition accuracy increases proportionally to code length.