The identification and classification of voltage and current disturbances are important tasks in the monitoring and protection of power systems. Most power quality disturbances are non-stationary and transitory and both detection and classification have proved to be very demanding. New intelligent system technologies that use wavelet transforms, expert systems and artificial neural networks include some unique advantages regarding fault analysis. This paper presents a new approach to classifying six classes of signals: five types of disturbance including sag, swell, transient, fluctuation, interruption, and the normal waveform. The concept of discrete wavelet transform for feature extraction from the power disturbance signal, combined with an artificial neural network and incorporating fuzzy logic to offer a powerful tool for detecting and classifying power quality problems, is introduced. The system was modeled using VHDL, a hardware description language, followed by extensive testing and simulation to verify the functionality of the system that allows efficient hardware implementation of the same. The extensive simulation results confirm the feasibility of the proposed algorithm. This method proposed herein classified and obtained 98.19% classification accuracy from the application of this system to software-generated signals and utility sampled disturbance events.