An approach for identification of sea ice types in spaceborne synthetic aperture radar (SAR) image data is presented. The unsupervised classification approach involves cluster analysis for segmentation of the image data followed by cluster labeling based on previously defined look-up tables containing the expected backscatter signatures of different types measured by land-based scatterometer. The particular look-up table used for labeling a segmented image is selected based on the seasonal and meteorological conditions at the time of data acquisition. The extensive scatterometer observations and experience accumulated in field campaigns during the last 10 year were used to construct these look-up tables. These tables are expected to evolve as sea ice observatins from the European ERS-1 SAR become available. This paper presents the classification approach, its expected performance, the dependence of this performance on radar system performance, and expected ice scattering characteristics. Results using both aircraft and simulated ERS-1 SAR data are presented. The results are compared to limited field ice property measurements and coincident passive microwave imagery. An algorithm based on this experimental approach has been implemented in the geophysical processor system at the Alaska SAR Facility for classification of sea ice data in ERS-1 C band SAR data. The importance of an integrated postlaunch program for validation and improvement of this approach is discussed. ¿ American Geophysical Union 1992