In modern control systems, identification is an integral part of adaptive control where process models are adjusted using real-time operation data and control actions optimal with respect to some performance criterion are developed. A variety of identification methods based on mathematical statistics techniques have been developed. Algorithms optimal for certain classes of objects arid external disturbances were categorized dependent on the available a priori information about the control object. The limits of approximating models development and application were outlined. Against this background, the paper presents novel associative search techniques enabling the development of a new dynamic object's model on each time step rather than plant approximation pertaining to time. The model is build using the data samples from process history (associations) developed at the learning phase. The new techniques employs the models of human individual's (process operator's, stock analyst's or trader's) behavior based on professional knowledge formalization. Application examples from oil refining and chemical industries, power engineering, arid banking are adduced.