A novel batch process monitoring approach based on kernel independent component analysis（kernel ICA）and local modeling was proposed.Kernel ICA is an improved independent component analysis（ICA）.It uses contrast functions based on canonical correlations in a reproducing kernel Hilbert space，and uses kernel trick to compute and optimize the contrast functions in this space.Consequently，kernel ICA is more efficient than ICA for varying source distributions.For the batch-wise unfolding normal batch process data，kernel ICA was employed to extract independent components at every time interval，and calculate the I2 and SPE statistics and their control limits.This modeling method did not need to predict the future observations，and the more important was that it could handle the problem of kernel ICA with the high dimension historical normal data.The simulation results showed the feasibility and efficiency of the new method，and the advantages of kernel ICA over conventional MICA method.