Existing approaches to biometric classification with quality measures make a clear distinction between the single-modality applications and the multi-modal scenarios. This study bridges this gap with Q-stack, a stacking-based classifier ensemble, which uses the class-independent signal quality measures and baseline classifier scores in order to improve the accuracy of uni- and multi-modal biometric classification. The seemingly counterintuitive notion of using class-independent quality information for improving class separation by considering quality measures as conditionally relevant classification features is explained. The authors present Q-stack as a generalised framework of classification with quality information, and argue that existing methods of classification with quality measures are its special cases. The authors further demonstrate the application of Q-stack on the task of biometric identity verification using face and fingerprint modalities, and show that the use of the proposed technique allows a systematic reduction of the error rates below those of the baseline classifiers, in scenarios involving single and multiple biometric modalities.