This paper describes a system for the recognition of sleep apnoea episodes from ECG signals using a committee of extreme learning machine (ELM) classifiers. RR-interval parameters (heart rate variability) have been used as the identifying features as they are directly affected by sleep apnoea. The MIT PhysioNet Apnea-ECG database was used. A committee of five ELM classifiers has been employed to classify one-minute epochs of ECG into normal or apnoeic epochs. Our results show that the classification performance from the committee of networks was superior to the results of a single ELM classifier for fan-outs from 1 to 100. Classification performance reached a plateau at a fan-out of 10. The maximum accuracy was 82.5% with a sensitivity of 81.9% and a specificity of 82.8%. The results were comparable to other published research with the same input data.