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The dispersive scattering centre (DSC) model characterises high-frequency backscatter from radar objects as a finite sum of localised scattering geometries distributed in range. These geometries, along with their locations, can be conveniently used as features in a one-dimensional automatic object recognition algorithm. The DSC model's type and range parameters correspond to geometry and distance features according to the geometric theory of diffraction (GTD). To demonstrate the viability of feature extraction based on the DSC model's range and type parameters, a typical object classification experiment was performed. The experimental data contained direct range radar measurements of four model fighter aircraft of similar size and shape at 0° elevation and 0°-30° azimuth. After implementing DSC model feature extraction on these data, a fully-connected two-layer neural net obtained over 98% classification accuracy. In addition, DSC model feature extraction gave an approximately 85% reduction in the number of required features when compared to the numerous range bin magnitudes used in template matching techniques.