Abstract: An adaptive plasma characterization system and method characterize a semiconductor plasma process using fuzzy logic and neural networks. The method includes the step of collecting input and output training data, where the input training data is based on variables associated with electrical power used to control a plasma chamber and results from execution of the plasma process. The method further includes the step of generating fuzzy logic-based input and output membership functions based on the training data. The membership functions enable estimation of an output parameter value of the plasma process, such that the membership functions characterize the plasma process with regard to the output parameter. Modifying the membership functions based on a neural network learning algorithm and output data provides ability to learn. Thus, etching process parameters such as etch rate, end point detection, and chamber maintenance can all be characterized in a manner that allows the system to operate autonomously.