Monitoring and management of process transitions is a critical activity in chemical plants due to increased potential for abnormal operations. This activity is often hampered by the lack of a proper approach to label the transition states. In this paper, we present a systematic framework that constructs process transition states thus facilitating their monitoring for faulty operations. To address the nonstationary and non-Gaussian characteristics of the time series data collected during the transition process, an ensemble clustering method based on dynamic k-principal component analysis-independent component analysis (k-ICA-PCA) models is proposed to enable labeling of transitions. Next, we combine a PCA-based dimension reduction with a pattern classification strategy based on multiclass support vector data description (SVDD) to achieve transition process monitoring. The Tennessee Eastman (TE) benchmark process is used as a case study to evaluate the performance of the proposed framework.