The　late　opportunities　prompted　by　artificial　intelligence　have　motivated　the　current　research　about　structural　damage　detection　strategies　based　on　damage-sensitive　subspace-based　indicators　(DI).　Precisely,　three　different　methodologies　(A),　(B),　and　(C)　are　discussed　for　multiclass　damage　classification　with　a　multi-layer　perceptron　(MLP)　network.　Specifically,　the　network＇s　inputs　combine　vibration　response　statistics　with　subspace-based　features.　Method　(A)　relies　on　statistical　features　only,　whereas　method　(B)　also　considers　the　most　informative　subspace-based　DI,　retrieved　from　an　empirical　sensitivity　analysis.　Finally,　method　(C)　provides　a　new　perspective　by　overcoming　the　arbitrary　choice　of　parameters　affecting　the　subspace-based　DIs　computation.　These　three　methods　are　tested　on　a　numerical　benchmark　problem,　and　the　results　emphasize　the　last　approach　as　the　most　promising　methodology.　For　the　sake　of　further　validation　purposes,　the　three　methods　have　been　finally　tested　on　an　experimental　steel　I-beam　setup,　evidencing　the　effectiveness　of　informative　subspace-based　DIs.