In areas aa diverse as remote sensing, astronomy, and medical imaging, image acquisit~ion technology has undergone tremendous improvements in recent years in terms of imaging resolution, hardware miniaturization, and computational speed. For example, current and future near-earth and planetary observation systems will return vast amounts of scientific data, a potential treasure-trove for scientific investigation and analysis. Unfortunately, advances in our ability to deal with this volume of data in an effective manner have not paralleled the hardware gains. While special-purpose tools for particular applications exist, there is a dearth of useful general-purpose software tools and algorithms which can assist a scientist in exploring large scientific image databases. At JPL we are currently developing interactive semi-automated image database exploration tools based on pattern recognition and machine learning technology. In this paper we discuss the general problem of automated image databasc exploration, the particular aspects ,of image databases which distinguish them from other databases, and how this impacts the application of off-the-shelf learning algorithms to problems of this nature. Current progress will be illustrated using two large-scale image exploration projects at JPL. The paper concludes with a discussion of current and future challenges.