Automatic target recognition from high range resolution radar profiles remains an important and challenging problem. In this paper, we present a novel feature set for this task that combines a noise-robust superresolution characterisation of the target scattering centres derived using the MUSIC algorithm with a representation of the target's radar shadow shape. To obtain the shadow shape features, three alternative spectral estimation methods are investigated. Using a hidden Markov model to represent aspect dependence, we demonstrate that the inclusion of the shadow features results in a significant improvement in recognition performance. Using azimuth apertures of 3° and 6° in a 10-target classification task from the MSTAR database, we obtain overall classification error rates of 1.3% and 0.2% respectively. These results are significantly better than those obtained by other published methods on the same database.