While human listening is robust in complex auditory scenes, current speech segregation algorithms do not perform well in noisy and reverberant environments. This paper addresses the robustness in binaural speech segregation by employing binary classification based on deep neural networks (DNNs). We systematically examine DNN generalization to untrained configurations. Evaluations and comparisons show that DNN based binaural classification produces superior segregation performance in a variety of multisource and reverberant conditions.