This　article　proposes　a　multifeature-assisted　neuro-transfer　function　(neuro-TF)　surrogate-based　electromagnetic　(EM)　optimization　technique　exploiting　trust-region　algorithms　for　microwave　filter　design.　The　proposed　optimization　technique　addresses　the　situation　where　the　response　of　the　starting　point　is　far　away　from　the　design　specifications.　We　propose　to　utilize　multiple　feature　parameters　to　help　move　the　passband　of　the　filter　response　into　the　range　of　design　specifications.　The　pole-zero-based　neuro-TF　is　introduced　in　this　article　to　help　extract　the　multiple　feature　parameters　when　the　feature　parameters　of　filter　responses　are　not　explicitly　identified.　Furthermore,　we　propose　to　derive　new　optimization　objective　functions　to　involve　the　multiple　feature　parameters.　A　new　trust-region　updating　formulation　for　the　modified　optimization　objective　functions　is　derived　to　guarantee　the　optimization　convergence.　With　the　assistance　of　multiple　feature　parameters,　the　proposed　surrogate-based　EM　optimization　has　a　better　capability　of　avoiding　local　minima　and　can　reach　the　optimal　EM　solution　faster　than　the　surrogate-based　EM　optimizations　without　feature　assistance.　Three　examples　of　EM　optimizations　of　microwave　filters　are　used　to　demonstrate　the　proposed　technique.