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Assessing the impact of ageing on biometric systems is an important challenge. In this paper, an i-vector speaker verification framework is used to evaluate the impact of long-term ageing on state-of-the-art speaker verification. Using the Trinity College Dublin Speaker Ageing (TCDSA) database, it is observed that the performance of the i-vector system, in terms of both discrimination and calibration, degrades progressively as the absolute age difference between training and testing samples increases. In the case of male speakers, the equal error rate (EER) increases from 4.61% at an ageing difference of 0–1 years to 32.74% at an age difference of 51–60 years. The performance of a Gaussian Mixture Model - Universal Background Model (GMM-UBM) system is presented for comparison. It is shown that while the i-vector system outperforms the GMM-UBM system, as absolute age difference increases, the performance of both degrades at a similar rate. It is concluded that long-term ageing variability is distinct from everyday intersession variability, and therefore must be dealt with via dedicated compensation strategies.