It is important to understand how we can deal with elements for the modeling of neural networks when we are investigating the dynamical performance and the information processing capabilities. The information processing capabilities of model neural networks will change for different response, synaptic weights or learning rules. Using the statistical neurodynamics method, we evaluate the capabilities of neural networks in order to understand the basic concept of parallel distributed processing. In this paper, we explain the results of theoretical analysis of the effects of the hysteretic property on the performance of sequential associative neural networks.