A major problem with multi-spectral satellite imagery is that images in many of the spectral channels contain redundant information about the atmosphere. By using principal component (PC) analysis to transform multi-channel satellite images, this information redundancy can be reduced. PC image (PCI) analysis finds the information that is common among the various channel images and puts that information into the PCIs in descending order of significance. The first PCI contains common information, leaving other information for higher-ordered PCIs. A second PCI is then formed which contains information common to the channel images other than that explained by the first PCI. The process continues until the number of PCIs is equal to the number of channel images being transformed. If none of the original channel images contains redundant information, then a PCI transformation is not needed. However, this is not the case with most satellite images. Because of channel image redundancy, the number of useful PCIs is often less than the number of channel images being transformed. The highest-order PCIs may contain only noise or slight differences among some of the channel images, however, these differences are of importance, especially for less obvious meteorological features in the atmosphere. Many interesting examples of PCIs created from GOES-8/9 imager and sounder data are possible. For the 5-channel GOES imager, the interpretation of the PCIs is fairly predictable when they are created on a large spatial scale. The main difference in interpretation occurs between day and night when the presence or lack of visible radiation is an important factor. For the GOES sounder, the interpretation of the PCIs is more complex, considering that input can consist of up to 19 channel images. Different subsets of the sounder channel images result in different products. With proper selection of channel images, emphasis can be placed upon either temperature or water vapor features in the atmosphere, or on features of the ground surface or clouds.