We present a post-processing technique for correcting errors committed by an arbitrary continuous speech recognizer. The technique leverages our observation that consistent recognition errors arising from mismatched training and usage conditions can be modeled and corrected. We have implemented a post-processor called SpeechPP to correct word-level errors, and we show that this post-processing technique applies successfully when the training and usage domains differ even slightly; for the purposes of the recognizer, such a difference manifests itself as differences in the vocabulary and in the likelihoods of word collocations. We hypothesize that other differences between the training and usage conditions yield recognition errors with some consistency also. Hence, we propose that our technique be used to compensate for those mismatches as well.