Macquarie University’s contribution to the QAst track of CLEF is centered on a study of Named Entity (NE) recognition on speech transcripts, and how such NE recognition impacts on the accuracy of the final question answering system. We have ported AFNER, the NE recogniser of the AnswerFinder question-answering project, to the types of answer types expected in the QAst track. AFNER uses a combination of regular expressions, lists of names (gazetteers) and machine learning. The machine learning component is a Maximum Entropy classifier and was trained on a development set of the AMI corpus. Problems with scalability of the system and errors of the extracted annotation lead to relatively poor performance in general, though the system was second (out of three participants) in one of the QAst subtasks.