This　paper　presents　performance　prediction　and　optimization　of　an　organic　Rankine　cycle　(ORC)　for　diesel　engine　waste　heat　recovery　based　on　artificial　neural　network　(ANN).　An　ANN　based　prediction　model　of　the　ORC　system　is　established　with　consideration　of　mean　squared　error　and　correlation　coefficient.　A　test　bench　of　combined　diesel　engine　and　ORC　waste　heat　recovery　system　is　developed,　and　the　experimental　data　used　to　train　and　test　the　proposed　ANN　model　are　collected.　A　genetic　algorithm　(GA)　is　also　considered　in　this　study　to　increase　prediction　accuracy,　and　the　ANN　model　is　evaluated　with　different　learning　rates,　train　functions　and　parameter　settings.　A　prediction　accuracy　comparison　of　the　ANN　model　with　and　without　using　GA　is　presented.　The　effects　of　seven　key　operating　parameters　on　the　power　output　of　the　ORC　system　are　investigated.　Finally,　a　performance　prediction　and　parametric　optimization　for　the　ORC　system　are　conducted　based　on　the　proposed　ANN　model.　The　results　show　that　prediction　error　of　the　ANN　model　with　using　the　GA　is　lower　than　that　without　using　GA.　Therefore,　it　is　recommended　to　optimize　the　weights　of　the　ANN　model　with　GA　for　a　high　prediction　accuracy.　The　proposed　ANN　model　shows　a　strong　learning　ability　and　good　generalization　performance.　Compared　to　the　experimental　data,　the　maximum　relative　error　is　less　than　5%.　The　experimental　results　after　optimizing　the　operating　parameters　are　very　close　to　ANN＇s　predictions,　indicating　one　or　more　operating　parameters　can　be　adjusted　to　obtain　a　higher　power　output　during　the　experiment　process.