This　paper　proposes　a　novel　index,　the　extreme　value　of　the　largest　principal　component　scores　of　the　generalized　likelihood　ratio　based　on　the　statistical　process　control　chart,　to　develop　a　structural　stiffness　identification　method　for　assessing　traditional　Chinese　mortise-tenon　joints.　The　proposed　method　involves　four　stages.　First,　a　generalized　likelihood　ratio　test　is　conducted　by　transforming　the　collected　acceleration　signals　into　the　generalized　likelihood　ratio　matrix.　Second,　principal　component　analysis　(PCA)　is　used　to　reduce　data　dimensionality　and　extract　the　extreme　values　of　the　first　principle　component　scores　as　a　novel　control　index.　Subsequently,　a　statistical　process　control　chart　is　drawn　via　the　proposed　control　index.　Finally,　the　ratio　of　the　structural　stiffness　reduction　can　be　evaluated　by　establishing　the　relationship　between　the　stiffness　and　number　of　control　indices　outside　the　upper　and　lower　control　limits　in　the　statistical　process　control　chart.　The　proposed　method　is　validated　by　vibration　test　data　acquired　from　a　traditional　timber　frame　under　reversed　cyclic　loads　and　vibration　in　a　laboratory.　The　results　show　that　(1)　the　proposed　index　performed　in　the　statistical　process　control　chart　is　able　to　monitor　the　novelty　of　the　mortise-tenon　timber　joint;　and　(2)　the　proposed　method　can　be　used　to　assess　the　states　of　the　timber　structure　and　even　predict　further　structural　stiffness.　©　2018　American　Society　of　Civil　Engineers.