To achieve self-organizing control based on fuzzy rules, we propose a fuzzy associative memory system called FAMOUS (Fuzzy Associative Memory Organizing Units System). FAMOUS simulates the knowledge representation and inference process by using fuzzy notation and by association in neural networks. FAMOUS's learning algorithm uses training steps to generate operation skills by modifying the expert knowledge that is initially built-in. A set of fuzzy if-then rules is used for controlling variable parameter processes. The control knowledge is represented as pairs consisting of a ‘condition’ in the if-part and an ‘operation (controller)’ in the then-part. The controllers are designed for optimization and stabilization in specific conditions. The fuzzy controller described in FAMOUS recalls well-trained controllers associated with the input condition and makes the final control output by synthesizing the intermediate outputs of their controllers. FAMOUS can highly refine knowledge by using neural network learning algorithms. In the if-part of the knowledge pairs, the membership function is automatically generated from input data by an unsupervised learning algorithm. In the then-part, each controller is individually trained to perform optimally under a specific condition and to satisfy the constraints of stabilization. To check that the whole controller stabilizes the parameter variance process, we also discuss how to obtain the class of stabilizers. Finally, we apply the proposed method to the control of a small helicopter (which is a variable parameter process) and show its usefulness in designing the controller.