Modern machine translation techniques rely heavily on parallel corpora, which are commonly harvested from the web. Such harvested corpora commonly exhibit problems in encoding. language identification, sentence alignment, and transliteration. Just as agricultural harvests must be threshed and winnowed to separate grain from chaff, electronic harvests should be carefully processed to ensure the quality and usability of the resulting corpora. In this work. we catalog a taxonomy of problems commonly found in harvested parallel corpora, and outline approaches for detecting and correcting these problems. This work is motivated by the lack of a standardized field guide outlining best practices for curating parallel corpora, especially those harvested from the web. Even the most-well curated parallel corpus is likely to contain some problems; even Europarl (Koehn, 2005), arguably the most widely examined parallel corpus, has undergone eight distinct revisions since its release in 2005. While this work is by no means comprehensive of all problems extant in corpus creation and curation, we nevertheless believe that a practical taxonomic field guide, laying out likely pitfalls awaiting corpus curators will represent an important contribution to our community.