We present a bimodal model able to internally simulate expected sequences of perceptions within a modality likely to follow the last sensory experience. Simultaneously reasonable perceptual expectations are elicited in the other modality. Our model is based on the novel Associative Self-Organizing Map (A-SOM), which develops a representation of the input space as well as learns to associate its activity with the (possibly time delayed) activities of an arbitrary number of other neural networks. The model was trained on sequences of inputs and the trained model was able to simulate appropriate sequences of activity patterns in the absence of sensory input for several epochs in both modalities. The simulation results are very encouraging and confirms the abilities of the A-SOM to serve in a multimodal system capable of internal simulation of perceptions and of cross-modal expectations.