We have developed a web-based decision support tool, Zone Mapping Application for Precision Farming (ZoneMAP, http://zonemap.umac.org), which can automatically determine the optimal number of management zones and delineate them using satellite imagery and field survey data provided by users. Application rates, say for fertilizer, can be prescribed for each zone and downloaded in a variety of formats to ensure compatibility with GPS-enabled farming applicators. ZoneMAP is linked to Digital Northern Great Plains, a web- based application which hosts a rich archive of satellite imagery from Landsat, MODIS, and ASTER, as well as high resolution airborne imagery from AeroCAM and AgCam. ZoneMAP transparently handles projection conversion, grid resampling, and spatial subsetting for data from a variety of sources. We used an unsupervised clustering method, Fuzzy C mean (FCM), for classification. We tested two cluster estimation algorithms and found that the diagonal distance algorithm gives more consistent results than Mahalanobis distance. We also evaluated efficacy of ZoneMAP using real field data provided by end users. Management zones created by ZoneMAP mapped natural variation of the soil organic matter and other nutrients relatively well, and are consistent with zone maps created by the users. The results demonstrated that ZoneMAP can serve as an effective yet easy-to-use tool for those who want to practice precision agriculture.