In　order　to　realize　the　non-contact　measurement　of　ceramic　insulator　contamination　severity,　a　method　based　on　feature　level　fusion　of　infrared　(IR)　and　ultraviolet　(UV)　image　information　is　proposed　in　this　paper.　IR　and　UV　images　of　artificially　polluted　insulators　were　obtained　from　high　voltage　experiments　at　80%,　85%　and　90%　RH.　After　the　preprocessing　of　images,　IR　and　UV　features　were　calculated,　respectively.　Then,　feature　selection　based　on　Fisher　criterion　was　adopted　to　gain　features,　which　have　the　ability　to　distinguish　different　contamination　grades　effectively.　In　feature　level　fusion　section,　kernel　principal　component　analysis　(KPCA)　was　applied　to　the　dimensionality　reduction　fusion　of　IR　and　UV　features　and　obtain　three-dimensional　fused　features.　A　particle　swarm　optimized　back　propagation　neural　network　(PSO-BPNN)　classifier　was　constructed　and　trained　to　recognize　the　contamination　grades.　Experimental　results　indicate　that　the　feature　level　fusion　of　IR　and　UV　information　based　on　KPCA　has　capability　to　characterize　the　contamination　grades　comprehensively.　Compared　with　recognition　using　IR　or　UV　features　separately,　recognition　based　on　the　feature　level　fusion　is　more　accurate　and　effective.　This　study　provides　a　new　methodology　for　the　measurement　of　insulator　contamination　severity　at　working　condition.