Inspired　by　the　success　of　deep　neural　net-works(DNN)　in　image　classification　tasks,　we　proposed　a　multivariate　time　series　classification(MTSC)　method,　which　fulfills　the　indefinite-length　and　indefinite-dimensional　time　series　classification　by　transforming　time　series　into　gray　images　and　classifying　images　by　image　classification　neural　networks.　We　explored　two　frequently　used　image　classification　neural　networks　on　two　imaging　methods　and　explored　transfer　learning　performance　when　we　pre-trained　the　networks　on　ImageNet　dataset.　Both　networks　in　the　experiments　achieves　competitive　results　compared　to　the　previous　time　series　classification　networks.　The　experimental　results　show,　by　using　fix-sized　images,　ResNet18　achieves　2.54%　improvement　in　overall　average　accuracy　compared　with　the　other　deep　neural　network　models.　©　2019　IEEE.