In　recent　years,　increasing　attention　has　been　paid　to　devising　tone　mapping　operators,　which　convert　specialized　high　dynamic　range　(HDR)　images　to　standard　low　dynamic　range　(LDR)　ones　for　visualization　on　daily　monitors.　However,　there　lacks　a　reliable　evaluation　criterion　for　comparing　distinct　tone　mapping　operators,　which　is　of　great　significance　to　the　design　and　optimization　of　tone　mapping　methods.　In　this　paper　we　propose　an　effective　tone　mapping　evaluation　system　(TMES)　based　on　a　two-stage　framework.　In　the　first　stage,　features　are　extracted　in　view　of　the　observations　that　luminance　information,　color　saturation,　statistical　naturalness,　structural　fidelity　and　visual　saliency　have　different　and　determinate　influences　on　the　perceptual　quality　of　tone-mapped　LDR　images.　In　the　second　stage,　the　extracted　features　are　integrated　with　a　data-driven　optimization　strategy,　which　iteratively　learns　the　parameters　by　applying　thousands　of　collected　tone-mapped　LDR　and　natural　images.　Our　TMES　evaluation　system　can　be　implemented　with　or　without　reference　HDR　images,　serving　for　the　optimization　and　monitoring　of　tone　mapping　methods.　Experiments　conducted　on　three　databases　prove　the　superiority　of　our　quality　evaluation　system.　©　2021,　Springer　Nature　Singapore　Pte　Ltd.