To date, automatic target recognition (ATR) techniques in synthetic aperture radar (SAR) imagery have largely focused on features that use only the magnitude part of SAR’s complex valued magnitude-plus-phase history. While such techniques are often very successful, they inherently ignore the significant amount of discriminatory information available in the phase. This paper describes a method for exploiting the complex information for ATR by using a convolutional neural network (CNN) that accepts fully complex input features. We show a performance leap from 87.30% to 99.21% accuracy on real collected wide-angle SAR data with the use of complex features.