In this research, we have used a combination of hyperspectral imaging and mathematical processing techniques to isolate a fluorescence signal from autofluorescence background. We have designed a hyperspectral excitation-scanning imager that has been used to image multiple fluorescence signals in mouse models in the presence of high autofluorescence background. Visualization, analysis, and classification software has also been designed to allow separation of fluorescence signal from autofluorescence background and discrimination amongst multiple fluorescence signals of interest. This presentation will include a summary of the current hyperspectral imager design, optimization and construction as well as a description of the visualization, analysis, and classification methods implemented. Finally, a discussion of current applications and possibilities for translation of hyperspectral technology to the clinical imaging arena will be presented.