In　this　paper,　we　consider　the　problem　of　variable　selection　for　partially　varying　coefficient　single-index　model,　and　present　a　regularized　variable　selection　procedure　by　combining　basis　function　approximations　with　smoothly　clipped　absolute　deviation　penalty.　The　proposed　procedure　simultaneously　selects　significant　variables　in　the　single-index　parametric　components　and　the　nonparametric　coefficient　function　components.　With　appropriate　selection　of　the　tuning　parameters,　the　consistency　of　the　variable　selection　procedure　and　the　oracle　property　of　the　estimators　are　established.　Finite　sample　performance　of　the　proposed　method　is　illustrated　by　a　simulation　study　and　real　data　analysis.