We present different techniques of fuzzy rule generation using the information we can obtain from the fuzzy clustering of a set of data which describe the behavior of a given system. The methods all try to obtain a first model of the consisted system that is good enough to serve as a first approximation for inference purposes. Thus, it is important that the methods should be as simple as possible but with great approximate power.