In a competitive power industry, power quality is a fundamentally important issue as customers can choose their electric energy suppliers based on the cost and quality. Power quality is already a major concern for industries with very sensitive equipment. Studies have been conducted on the definitions and classifications of power quality problems. Instrumentation for monitoring and detection of power quality problems is also commercially available. However, analytical tools that can help determine the causeeffect relations for power quality problems are still in the development stage. Intelligent system techniques are natural tools for power quality assessment. This presentation will include an analysis of how intelligent system tools fit into various aspects of power quality analysis. Power quality analysis involves the analysis of voltage and current waveforms, qualitative behaviors of distribution systems and protective devices, and the behavior of different types of load. 'Power rules' have been used to identify the events leading to power quality problems. Rule-based systems are wellestablished intelligent system tools that can be used for power quality analysis. Artificial neural networks are capable tools for detection of transients or harmonics based on the voltage or current waveforms. Most power quality problems are associated with the distribution systems. Power quality monitoring instrumentation may be available at substations or important customer sites. However, the level of monitoring usually would not extend much beyond the substations or important customer sites. Therefore, a high level of uncertainly is expected for power quality assessment. Fuzzy logic provides the necessary tools to handle such uncertainties.