We model neural dynamical behavior during object perception using the principle of sparse coding in multilayer oscillatory networks. The network model consists of units with amplitude and phase variables, and allows the propagation of higher-level information to lower levels via feedback connections. We show that this model can replicate findings in the neuroscience literature, where measurements have shown that neurons in lower level visual areas respond in a delayed fashion to missing contours of whole objects. We contrast the behavior of feedback connections with that of lateral connections by selectively disabling these in our model to examine their contributions to object perception. This paper successfully extends the previously reported capabilities of oscillatory networks by applying them to model perceptual tasks. © 2011 IEEE.