Automatic lipreading has major potential impact for speech recognition, supplementing and complementing the acoustic modality. Most attempts at lipreading have been performed on small vocabulary tasks, due to a shortfall of appropriate audio-visual datasets. In this work we use the publicly available TCD-TIMIT database, designed for large vocabulary continuous audio-visual speech recognition. We compare the viseme recognition performance of the most widely used features for lipreading, Discrete Cosine Transform (DCT) and Active Appearance Models (AAM), in a traditional Hidden Markov Model (HMM) framework. We also exploit recent advances in AAM fitting. We found the DCT to outperform AAM by more than 6% for a viseme recognition task with 56 speakers. The overall accuracy of the DCT is quite low (32-34%). We conclude that a fundamental rethink of the modelling of visual features may be needed for this task.