The Progressing Cavity Pump artificial lift method for oil well production is well known in the oil industry, despite that a poor literature about PCP analysis is available. PCP automation based on ladder programming deploys information such as instantaneous rod torque, production line pressure, and rods rotation to name a few. The use of this information to analyze the well performance, identify imperfections and problems depends on a deep knowledge about the system well-PCP, but the procedures to do so are yet to be developed. To optimize the production and increase the mean time between failures in this type of well is necessary not only to improve the algorithm coded on the ladder programming loaded in the PCP automation PLC but also to develop a knowledge base where the skills and knowledge of the local PCP specialists could be easily coded. To analyze data deployed by the PLC and to improve the firmware customization, an intelligent system is being developed. This intelligent system is based on artificial intelligence concepts such fuzzy functions, coded in a way to easy acquire the PCP specialist knowledge on how to understand what is going on in the well and what to do to bring it back to a proper behavior. The present paper presents an intelligent system for automation of PCP wells capable of acquire and spread the knowledge of the referring specialist in diagnosis and solutions of problems during the well operation. Fuzzy agents are proposed in order to do an intelligent analysis of a set of PCP variables such as friction, motor, pump position, production and volumetric efficiency, ... The paper also describes the tools and interfaces designed to acquire the PCP specialist knowledge, the system implementation, and an acquired knowledge base example. Introduction Progressing Cavity Pumping (PCP) control is simpler than the control used in Sucker Rod Pumping (SRP) where a customized firmware is needed and demands a far more complicated analysis [4,6]. All the functionality needed to implement the PCP control can be obtained using ladder logic programming language. Ladder logic programming language is a generic programming language to be used in Programmable Logic Controllers (PLC) and is a well know and low cost development programming language. All the well data generated by the Ladder Program, see figure below, are collected by a communication driver and stored in a Data-base at the central office, in a non stop sequential pooling. This data-base is merged to others data-base such as production and well servicing data-bases through a corporate network. The resulted data-base is the data-source to the analysis presented in this paper. A Mamdani fuzzy structure [1] was chosen to represent the knowledge needed to analyze all the data gathered. The Artificial Lift knowledge collected from Artificial Lift Specialists (ALS) was broken in to small pieces and coded as Fuzzy Agents (FA), e.g. specialized agents to analyze hypothesis about well characteristics that has impact on the PCP performance such as friction, gas interference, motor HP, pumping efficiency, pump depth, paraffin, scale, sand deposition and corrosion [6]. This work extends the PCP analysis presented by Carvalho et alli [5] to an all Fuzzy approach. Fuzzy Concepts There are two popular fuzzy structures commonly used: the Mamdani [1] and Takagi-Sugeno (TS) [2] fuzzy structure. In this paper, the first one is adopted.