This chapter discusses intelligent design techniques for predictive identification models of nonlinear dynamic plants. The techniques are based on inductive learning, i.e., on knowledge retrieval from process data. An identification method named associative search is presented. It is based on data mining tech- niques and enables the use of all available process data development in the iden- tification model design. Process situations resembling the current one in the sense of close process variable values are being selected from the process history; such situations are called associations. Process data clustering improves the computing speed and saves computational resources.