Autofluorescence, also known as adventitious fluorescence or background fluorescence, often poses a significant problem in many applications of fluorescence microscopy. It contributes to unwanted noise and can overwhelm the desired signal. Particularly difficult samples to image include many pathology specimens that have been processed using crosslinking fixatives (typically formaldehyde). This procedure dramatically increase the autofluorescence level, leading to bright, broad spectrum emissions, particularly from connective tissue components. Unprocessed plant tissue and neuronal tissue also have extremely high levels of endogenous autofluorescence that can make many convenient labeling strategies, including most green fluorescent protein (GFP) labels, extremely problematic. Various solutions have been proposed for the reduction or elimination of autofluorescence. These include using narrow bandpass emission filters to try to isolate the desired fluorescence signal, the use of labels that can be excited at wavelengths that are much less likely to induce autofluorescence (moving the excitation towards the NIR is effective), and post-processing aldehyde-fixed samples with such reagents as sodium borohydride or toluidine blue to chemically suppress the autofluorescence signal. However, in many cases, these approaches are either infeasible or ineffective. Spectral imaging, that is, the acquisition of a high-resolution optical spectrum at every pixel of an image, offers another approach to elimination of the contribution of autofluorescence. This has recently become possible due to the development of a number of technologies that allow the collection of spectral datasets from fluorescently labeled samples using fluorescence microscopy. Such technologies include liquid crystal tunable filters (LCTFs) and Fourier transform imaging spectrometers, both of which are now commercially available.