AbstractFor dynamic batch process monitoring, a two-dimensional dynamic modeling framework has recently been formulated, which is based on a two-dimensional autoregressive model and the principal component analysis (PCA) method. Different from traditional dynamic batch process monitoring, the two-dimensional method can monitor both within batch and batch-to-batch dynamic information of the process data. However, this PCA-related method has two main restrictions, which may render poor monitoring performance in practice. First, it is under the assumption that the distribution of the process data is Gaussian. Second, the correlations between different process variables are assumed to be linear with each other. Unfortunately, both of these two assumptions are difficult to satisfy in batch processes. In this paper, support vector data description (SVDD) is incorporated into the two-dimensional modeling framework, which has no Gaussian limitation of the data, and can also model the nonlinear relationship between process variables. For dynamic batch process monitoring, a distance based statistic is proposed. Based on results of a simulation case study, the monitoring performance has been improved.