AbstractThe occurrence of instabilities in chemically reacting systems, resulting in unsteady and spatially inhomogeneous reaction rates, is a widespread phenomenon. In this article, we use nonlinear signal processing techniques to extract a simple, but accurate, dynamic model from experimental data of a system with spatiotemporal variations. The approach consists of a combination of two steps. The proper orthogonal decomposition [POD or Karhunen‐Loève (KL) expansion] allows us to determine active degrees of freedom (important spatial structures) of the system. Projection onto these “modes” reduces the data to a small number of time series. Processing these time series through an artificial neural network (ANN) results in a low‐dimensional, nonlinear dynamic model with almost quantitative predictive capabilities.This approach is demonstrated using spatiotemporal data from CO oxidation on a Pt (110) crystal surface. In this special case, the dynamics of the two‐dimensional reaction profile can be successfully described by four modes; the ANN‐based model not only correctly predicts the spatiotemporal short‐term behavior, but also accurately captures the long‐term dynamics (the attractor). While this approach does not substitute for fundamental modeling, it provides a systematic framework for processing experimental data from a wide variety of spatiotemporally varying reaction engin