This paper investigates the performance of blind signal separation (BSS) algorithms that exploit the temporal predictability of speech. Specifically, the investigation considers how the separation performance of two BSS algorithms will be affected when the length of the AR process (used in the algorithms to model speech) is varied. The investigation concludes that the length of the AR process (prediction order) has a significant impact on separation performance. In particular, the separation performance of both algorithms is degraded, if the AR model’s prediction order, over fits, or under fits, the temporal structure of the speech. It is revealed that a prediction order of 30- 50 provides maximum separation performance for natural speech, however a prediction order of 10 is more applicable if computational cost is a consideration.