Time-lapse microscopic movies are being increasingly utilized for understanding the derivation of cell states and predicting cell future. Often, fluorescence and other types of labeling are not available or desirable, and cell state-definitions based on observable structures must be used. We present the methodology for cell behavior recognition and prediction based on the short term cell recurrent behavior analysis. This approach has theoretical justification in non-linear dynamics theory. The methodology is based on the general stochastic systems theory which allows us to define the cell states, trajectory and the system itself. We introduce the usage of a novel image content descriptor based on information contribution (gain) by each image point for the cell state characterization as the first step. The linkage between the method and the general system theory is presented as a general frame for cell behavior interpretation. We also discuss extended cell description, system theory and methodology for future development. This methodology may be used for many practical purposes, ranging from advanced, medically relevant, precise cell culture diagnostics to very utilitarian cell recognition in a noisy or uneven image background. In addition, the results are theoretically justified.