A predicate model of the process knowledge of inter-period processing of radar signals in the detection and recognition of extended objects and a decision-making method based on precedents have been developed. The main features and structural elements of the process knowledge model are presented. It is shown that the advantages of this model are related to the configuration and hierarchical representation of the process for studying the possible structures of single or groups of impulse signals within the same radar field of view based on the intelligent analysis of signals using the algebra of finite predicates. It is shown how this approach can be used to automate the process of detecting and recognizing extended objects such as clouds, atmospheric inhomogeneities of the angel-echo type. A method for processing process knowledge has been developed as a tool for creating universal algorithms for inter-period processing of signal information to ensure effective detection and recognition of various extended objects, including atmospheric inhomogeneities of the angel-echo type, by accumulating both signal (energy) and logical information in the analyzed cell and in its vicinity. The developed technology includes procedures for formalizing and analyzing the symbolic model of observed objects for making decisions based on precedents. Depending on the types of connections used in the model, classifying and functional networks are distinguished, where some elements of logical and network models are used. The idea of inference rules or decision rules is borrowed from logical models, and the description of knowledge in the form of a semantic neural network is borrowed from network models. In this combined model, procedural information is clearly highlighted. Instead of a logical conclusion, a conclusion or a decisive rule on knowledge appears. As a result of solving the system of predicate equations of process knowledge, we find the place, geometric dimensions and type of the symbolic model of an extended object.