Tagger accuracy deteriorates when applied to texts different from the training corpus, e.g. with respect to register or time period. On historical data, accuracy can drop to and below 90%. We are tagging and parsing ARCHER, a historical corpus sampled from British and American texts from 1600-1999. We improve tagging accuracy by (1) using a version of the corpus that has been automatically mapped to PDE spelling with VARD, (2) by combining several part-of-speech taggers in an ensemble system – which improves tagging by about 1% over CLAWS and 2% over Tree-Tagger, and (3) by using a small amount of human intervention – which allows us to reach 98% accuracy from 1700 on.