Online steady state identification (SSID) is an important task to ensure the quality consistence of final products in batch processes. Additionally, it is also critical for satisfactory control of many batch processes. The existing approach for batch process SSID is based on the multiway principal component analysis (MPCA) method, which requires history data from dozens of batches for process modeling. Consequently, this limits its online applications. In this work, principal component analysis (PCA) models are built for each batch from which the variable correlation information is extracted. The changes in variable correlation structures are then quantified with a PCA similarity factor. At the same time, the Mahalanobis distances between batch trajectories are also calculated to indicate the changes in variable trajectory magnitudes. These two types of information are then used for online SSID in batch processes. This method is more suitable for online applications and can solve the problems of uneven operation durations. Additionally, it can be easily extended to deal with non-Gaussian information and multiphase batch process characteristics. Application examples show the effectiveness of the proposed method.