In　current　industrial　production,　the　acquisition　and　processing　of　large　amounts　of　sensor　monitoring　data　have　become　an　important　component　of　equipment　monitoring.　Artificial　intelligence　approaches　have　the　advantage　in　the　management　ability　of　big　data　from　manufacturing　processes　and　are　thus　increasingly　used　in　industry.　However,　the　artificial　intelligence　methods　are　suitable　for　specific　production　lines　and　hardly　generalizable　to　new　situations.　To　solve　these　problems,　this　paper　proposes　a　transfer　learning-based　Stacked　Auto-encoder　(SAE)　with　Convolutional　Neural　Network　(CNN),　which　simplifies　high-dimensional　data　into　generalization　and　effective　features　that　characterize　the　machine　tool＇s　working　state.　First,　it　uses　an　unsupervised　SAE　for　general　feature　extraction　for　monitoring　data　from　different　production　lines.　Second,　a　few　supervised　data　are　used　to　fine-turning　a　working　condition　classifier　composed　of　a　one-dimensional　Convolutional　Neural　Network　to　verify　the　performance　of　the　model　in　the　new　production　line.　The　proposed　method　reduces　the　amount　of　labeled　training　data　while　at　the　same　time　enhancing　the　accuracy.　The　experiment　result　shows　a　better　performance　of　the　proposed　method,　which　suggests　a　strong　potential　for　applications　in　pattern　recognition　during　sheet　metal　forming　processes.　©　2022　IEEE.