Our paper will present an analysis of the utility of multispectral imagery versus standard RGB imagery for routine H&Estained histopathology imagery, in particular for the classification of histologic classes, with a focus on nuclei detection. Our multispectral data consists of 29 spectral bands, spaced 10 nm within the visual range of 420-700 nm. It is hypothesized that the additional spectra contains further information useful for classification as compared to the 3 standard bands of RGB microscopy imagery; this has been established in other application domains, e.g., remote sensing. We will present analyses of our data designed to test this hypothesis. In brief, we will use several standard classification techniques (maximum likelihood, spectral angle mapper, minimum Euclidean distance, and an automated feature extraction tool using evolutionary computation) on our multispectral image stacks, as well as on several types of derived RGB imagery. We will analyze the classification performance, as well as other applicable and informative metrics, such as the statistics of spectral bands chosen for classification and the entropy of different spectral bands within the class of interest.