This paper presents hyperspectral fluorescence imaging and a support vector machine for detecting skin tumors. Skin cancers may not be visually obvious since the visual signature appears as shape distortion rather than discoloration. As a definitive test for cancer diagnosis, skin biopsy requires both trained professionals and significant waiting time. Hyperspectral fluorescence imaging offers an instant, non-invasive diagnostic procedure based on the analysis of the spectral signatures of skin tissue. A hyperspectral image contains spatial information measured at a sequence of individual wavelength across a sufficiently broad spectral band at high-resolution spectrum. Fluorescence is a phenomenon where light is absorbed at a given wavelength and then is normally followed by the emission of light at a longer wavelength. Fluorescence generated by the skin tissue is collected and analyzed to determine whether cancer exists. Oak Ridge National Laboratory developed an endoscopic hyperspectral imaging system capable of fluorescence imaging for skin cancer detection. This hyperspectral imaging system captures hyperspectral images of 21 spectral bands of wavelength ranging from 440 nm to 640 nm. Each band image is spatially co-registered to eliminate the spectral offset caused during the image capture procedure. Image smoothing by means of a local spatial filter with Gaussian kernel increases the classification accuracy and reduces false positives. Experiments show that the SVM classification with spatial filtering achieves high skin tumor detection accuracies.