The　online　measurement　of　gas　and　liquid　flow　rates　in　wet　gas　is　of　great　significance.　This　paper　presents　a　new　method　to　measure　gas　and　liquid　flow　rates　of　wet　gas　by　combining　the　cone　throttle　device　and　machine　learning　techniques.　The　equivalent　diameter　ratio　of　the　cone　device　is　0.45.　Experiments　are　carried　out　in　a　horizontal　pipe　of　diameter　50　mm　and　the　operating　pressure　ranges　from　100　kPa　to　250　kPa.　The　working　fluids　are　the　mixture　of　air　and　water　with　the　Lockhart-Martinelli　parameter　(XLM)　less　than　0.3.　The　multilayer　feedforward　neural　network　is　used　for　developing　the　measurement　model.　The　model　requires　representative　features　as　inputs　and　uses　the　gas　and　liquid　flow　rates　as　outputs.　In　addition　to　the　mean　values　of　the　permanent　pressure　loss　and　the　upstream-throat　differential　pressure,　the　probability　density　function　(PDF)　and　power　spectral　density　(PSD)　of　the　upstream-throat　differential　pressure　fluctuation　are　also　extracted　as　representative　features.　With　the　principal　component　analysis　method,　the　independent　PDF　and　PSD　features　are　obtained.　The　untrained　dataset　is　used　to　evaluate　the　performance　of　the　neural　network　model.　Predictions　of　the　flow　rates　are　in　good　agreement　with　the　experiments.　The　mean　relative　errors　of　the　gas　and　liquid　flow　rates　are　0.05%　and　−3.66%,　respectively.　The　results　show　that　the　proposed　method　is　capable　of　establishing　the　implicit　correlations　between　the　characteristic　parameters　of　wet　gas　and　the　corresponding　flow　rates.　©　2020　Elsevier　Ltd