In this paper we present results of an analysis of the relative utility of statistical, structural, and frequency based second-order texture methods for discrimination of sea ice types in synthetic aperture radar (SAR) data. Algorithms were trained using a calibration data set and robustness of the methods were assessed by directry computing ice classes within a validation data set. Classification using a first-order approach (average grey level) produced Kappa classification accuracies of 51.0 and 33.0 percent for the calibration and validation data. The first-order approach is provided primarily as a reference from which to compare the second-order approaches because the test conditions were selected to be specifically difficult (i.e., different incidence angle ranges between calibration and validation images) for any approach using image tone or the relative scattering cross section