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We present a framework for automatic detection of oil spills in SAR images. Multi incident angle and multi polarization SAR data are ingested into the framework in order to optimize revisit times and thereby the temporal and spatial coverage. Dark spots in the images are primarily detected by adaptive thresholding. For each of them a number of features are computed in order to classify the slick as either an oil slick or a 'look-alike' (other oceanographic phenomena which resemble oil slicks). A classification scheme is utilized based on statistical modeling. A data set of about 100 images from each of the sensors ERS, Radarsat and ENVISAT is or will soon be available to train and test the algorithm. In this paper, only results from ERS and Radarsat are reported because the access of ENVISAT images has been delayed.