Multi-spectral imaging provides digital images of a scene or object at a large, usually sequential number of wavelengths, generating precise optical spectra at every pixel. We use the term "spectral signature" for a quantitative plot of optical property variations as a function of wavelengths. We present here intelligent spectral signature bio-imaging methods we developed, including automatic signature selection based on machine learning algorithms and database search-based automatic color allocations, and selected visualization schemes matching these approaches. Using this intelligent spectral signature bio-imaging method, we could discriminate normal and aganglionic colon tissue of the Hirschsprung's disease mouse model with over 95% sensitivity and specificity in various similarity measure methods and various anatomic organs such as parathyroid gland, thyroid gland and pre-tracheal fat in dissected neck of the rat in vivo .© (2007) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.