One of the key motivations in the construction of ever more sophisticated mortality models was the realisation of the importance of "cohort effects" in the historical data. However, these are often difficult to estimate robustly, due to the identifiability issues present in age/period/cohort mortality models, and exhibit spurious features for the most recent years of birth, for which we have little data. These can cause problems when we project the model into the future. In this study, we show how to ensure that projected mortality rates from the model are independent of the arbitrary identifiability constraints * Material in this paper was presented under the title "Projecting mortality: Identifiability with trend changes and cohort effects" at the 17 th International Congress on Insurance: Mathematics and Economics in July 2013 in Copenhagen, Denmark. We are grateful to participants at this conference, and to Matthias Börger, Frank van Berkum, Andrew Cairns, Pietro Millossovich and Andrés Villegas for useful discussions regarding this work.† This study was performed when Dr Hunt was a PhD student at Cass Business School, City University London, and therefore the views expressed within it are held in a personal capacity and do not represent the opinions of Pacific Life Re and should not be read to that effect. needed to identify the cohort parameters. We then go on to develop a Bayesian approach for projecting the cohort parameters, which allows fully for uncertainty in the recent parameters due to the lack of information for these years of birth, which leads to more reasonable projections of mortality rates in future.