This　paper　presents　a　methodology　to　predict　and　optimize　performance　of　an　organic　Rankine　cycle　(ORC)　using　a　back　propagation　neural　network　(BPNN)　for　diesel　engine　waste　heat　recovery.　A　test　bench　of　an　ORC　with　a　diesel　engine　is　established　to　collect　experimental　data.　The　collected　data　are　used　to　train　and　test　a　BPNN　model　for　performance　prediction　and　optimization.　After　evaluating　different　hidden　layers,　a　BPNN　model　of　the　ORC　system　is　determined　with　the　consideration　of　mean　squared　error　(MSE)　and　correlation　coefficient.　The　effects　of　key　operating　parameters　on　the　power　output　of　the　ORC　system　and　exhaust　temperature　at　the　outlet　of　the　evaporator　are　evaluated　using　the　proposed　model　and　further　discussed.　Finally,　a　multi-objective　optimization　of　the　ORC　system　is　conducted　for　maximizing　power　output　and　minimizing　exhaust　temperature　at　the　outlet　of　the　evaporator　based　on　the　proposed　BPNN　model.　The　results　show　that　the　proposed　BPNN　model　has　a　high　prediction　accuracy　and　the　maximum　relative　error　of　the　power　output　is　less　than　5%.　It　also　shows　that　when　the　operations　are　optimized　based　on　the　proposed　model,　the　power　output　of　the　ORC　system　can　be　higher　than　the　experimental　results.