Iris biometrics is widely regarded as a reliable and accurate method for personal identification and the continuing advancements in the field have resulted in the technology being widely adopted in recent years and implemented in many different scenarios. Current typical iris biometric deployments, while generally expected to perform well, require a considerable level of co-operation from the system user. Specifically, the physical positioning of the human eye in relation to the iris capture device is a critical factor, which can substantially affect the performance of the overall iris biometric system. The work reported in this study will explore some of the important issues relating to the capture and identification of iris images at varying positions with respect to the capture device, and in particular presents an investigation into the analysis of iris images captured when the gaze angle of a subject is not aligned with the axis of the camera lens. A reliable method of acquiring off-angle iris images will be implemented, together with a study of a database thereby compiled of such images captured methodically. A detailed analysis of these so-called “off-angle” characteristics will be presented, making possible the implementation of new methods whereby significant enhancement of system performance can be achieved. The research carried out in this study suggests that implementing carefully new training methodologies to improve the classification performance can compensate effectively for the problem of off-angle iris images. The research also suggests that acquiring off-angle iris samples during the enrolment process for an iris biometric system and the implementation of the developed training configurations provides an increase in classification performance.