Most existing methods for the state estimation in batch processes are similar to those for continuous processes, and these methods usually only consider the state dynamics within a single batch and ignore the dynamics across batches. In this paper, the state estimation in batch processes is investigated based on a two-dimensional state-space model by employing the Bayesian recursive algorithm. In addition to the dynamics along the time dimension (the dynamics within a single batch), the batch process is also characterized by the dynamics along the batch dimension (batch-to-batch dynamics). In the proposed method, both the batch-to-batch dynamics and the dynamics within a single batch are taken into account. The current state is dependent on the previous states both along the time dimension and along the batch dimension, so the filtering and smoothing for previous batches should be performed before doing current state estimation. In this way, the information on measurements from the previous batches as well as from the current batch can be incorporated into the estimation. The proposed method is illustrated and evaluated through a simple numerical example as well as a simulated two-state batch reaction process.