A statistical spectral band selection procedure and classifiers for an active multispectral laser radar (LADAR) sensor are described. The sensor will operate in the 1 to 5 mm wavelength region. The algorithms proposed are tested using library reflectance spectra for some representative background materials. The material classes considered include both natural (vegetation and soil) and man-made (camouflage cloth and tar-asphalt). The analysis includes noise statistics due to Gaussian receiver noise and target induced speckle variations in the LADAR return signal intensity. The results of this analysis are then directly applied to an artificially generated spatial template of a scene consisting of these four material classes. The performance of four different classifier algorithms, which include a minimum distance classifier, a logdomain minimum distance classifier, a Bayes speckle-only classifier, and a Bayes speckle-Gaussian classifier, are evaluated. We show that the Bayesian classifier designed for speckle and Gaussian noise statistics outperforms the other classifiers. Our results also indicate that even when exact knowledge of the observation model is available, the classifier performance for speckled images can be poor unless the number of integrated speckle cells is large.