One of the key requirements of real-time processing systems for remote sensors is the ability to accurately and automatically geo-locate events. This capability often relies on the ability to find control points to feed into a registration-based geo-location algorithm. Clouds can make the choice of control points difficult. If each pixel in a given image can be identified as cloudy or clear, the geo-location algorithm can limit the control point selection to clear pixels, thereby improving registration accuracy. Most cloud masking algorithms rely on a large number of spectral bands for good results, e.g., MODIS, whereas with our sensor, we have only three simultaneous bands available. This paper discusses a promising new approach to generating cloud masks in real-time with a limited number of spectral bands. The effort investigated statistical methods, spatial and texture-based approaches and evaluated performance on real remote sensing data. Although the spatial and texture-based approaches did not exhibit good performance due to sensor limitations in spatial resolution and too much variation in spectral response of both surface features and clouds, the statistical classification approach applied to only two bands performed very well. Images from three daytime remote sensing collects were analyzed to determine features that best separate pixels into cloudy and clear classes. A Bayes classifier was then applied to feature vectors computed for each pixel to generate a binary cloud mask. Initial results are excellent and show very good accuracy over a variety of terrain types, including mountains, desert, and coastline.