Operating　condition　detection　and　fault　diagnosis　are　very　important　for　reliable　operation　of　reciprocating　compressors.　Machine　learning　is　one　of　the　most　powerful　tools　in　this　field.　However,　there　are　very　few　comprehensive　reviews　which　summarize　the　current　research　of　machine　learning　in　monitoring　reciprocating　compressor　operating　condition　and　fault　diagnosis.　In　this　paper,　the　recent　application　of　machine　learning　techniques　in　reciprocating　compressor　fault　diagnosis　is　reviewed.　The　advantages　and　challenges　in　the　detection　process,　based　on　three　main　monitoring　parameters　in　practical　applications,　are　discussed.　Future　research　direction　and　development　are　proposed.