An adaptive process monitoring approach with variable moving window principal component analysis (variable MWPCA) is proposed. On the basis of recursively updating the correlation matrix in both samplewise and blockwise manners, the approach combines the moving window technique with the classical rank-r singular value decomposition (R-SVD) algorithm to construct a new PCA model. Compared with previous MWPCA algorithms, the method not only improves the computation efficiency but also reduces the storage requirement. Furthermore, instead of a fixed window size, a variable moving window strategy is described in detail for accommodating normal process changes with different changing rates. The proposed method is applied to an illustrative case and a continuous stirred tank reactor process, and the monitoring results show better adaptability to both a slow drift and a set-point change than the results of using the conventional MWPCA with a fixed window size.