A method of modelling accent-specific pronunciation variations is presented. Speech from an unseen accent group is phonetically transcribed such that pronunciation variations may be derived. These context-dependent variations are clustered in a decision tree which is used as a model of the pronunciation variation associated with this new accent group. The tree is then used to build a new pronunciation dictionary for use during the recognition process. Experiments are presented for the recognition of Lancashire&Yorkshire accented speech using a recognizer trained on London& South East England speakers. The results show that the addition of accent-specific pronunciations can reduce the error rate by almost 20% for cross accent recognition. It is also shown that worthwhile gains in performance can be obtained using only a small amount of accent-specific data.