In　order　to　handle　the　problem　of　nonstationary　and　random　nature　of　data　in　the　process　industry,　an　improved　multiscale　principal　component　analysis　is　proposed,　which　contains　different　noises　inevitably.　Firstly,　an　improved　wavelet　threshold　denoising　method　which　combines　multiple　wavelet　transform　with　a　new　threshold　function　based　on　the　characteristics　of　wavelet　analysis　is　proposed.　The　data　collected　from　the　industry　condition　are　processed　by　means　of　the　improved　wavelet　threshold　denoising　method.　Using　wavelets,　the　individual　variable　is　decomposed　into　approximations　and　details　at　different　scales.　Contributions　from　each　scale　are　collected　in　separate　matrices,　and　a　PCA　model　is　then　constructed　to　extract　correlation　at　each　scale.　According　to　the　simulation　of　Tennessee　Eastman,　and　comparing　the　improved　MSPCA　with　traditional　PCA,　it　shows　that　the　improved　MSPCA　has　enhanced　the　accuracy　of　process　monitoring.