The ability to automatically detect dust devils in the Martian atmosphere from orbital imagery is becoming important both for scientific studies of the planet and for the planning of future robotic and manned missions. This paper describes our approach for the unsupervised detection of dust devils and the preliminary results achieved to date. The algorithm centers upon the use of a filter constructed from Gaussian profiles to match dust devil characteristics over a range of scale and orientation. The classification step is designed to reduce false positive errors caused by static surface features such as craters. A brief discussion of planned future work is included. ?? 2006 IEEE.