Given a specific task, like detection of hidden objects (i.e. vehicles and landmines) in a natural background, hyperspectral data gives a significant advantage over RGB- color or gray-value images. It introduces however, a trade- off between cost, speed, signal-to-noise ratio, spectral resolution, and spatial resolution. Our research concentrates on making an optimal choice for spectral bands in an imaging system with a high frame rate and spatial resolution. This can be done using a real-time multispectral 3CCD camera, which records a scene with three detectors, each accurately set to a wavelength by selected optical filters. This leads to the subject of this paper: how to select three optimal bands from hyperspectral data to perform a certain task. The choice of these bands includes two aspects, the center wavelength, and the spectral width. A band-selection and band-broadening procedure has been developed, based on statistical pattern recognition techniques. We will demonstrate our proposed band selection algorithm, and present its classification results compared to red- green-blue and red-green-near-infrared data for a military vehicle in a natural background and for surface laid landmines in vegetation.