Another application of the extended Kalman filter recurrent multilayer perceptron (EKF‐RMLP) scheme is presented. The generation of a chaotic process is governed by a coupled set of nonlinear differential or difference equations. The hallmark of a chaotic process is sensitivity to initial conditions, which means that if the starting point of motion is perturbed by a very small increment, the deviation in the resulting waveform, compared to the original waveform, increases exponentially with time. Consequently, unlike an ordinary deterministic process, a chaotic process is predictable only in the short term. In this chapter, five data sets are considered: the logistic map, Ikeda map, and Lorenz attractor, whose dynamics are governed by known equations; and laser intensity pulsations and sea clutter (i.e., radar backscatter from an ocean surface), whose underlying equations of motion are unknown.