This study compares consistency in target texts produced using translation memory (TM) with that of target texts produced using statistical machine translation (SMT), where the SMT engine is trained on the same texts as are reused in the TM workflow. These comparisons focus specifically on noun and verb inconsistencies, as such inconsistencies appear to be highly prevalent in TM data. The study substitutes inconsistent TM target text nouns and verbs for consistent nouns and verbs from the SMT output to test whether this results in improvements in overall TM consistency and whether an SMT engine trained on the 'laundered' TM data performs better than the baseline engine. Improvements were observed in both TM consistency and SMT performance, a finding that indicates the potential of this approach for improving TM/MT integration. © 2013 Taylor & Francis.