This paper proposes a neural network approach for forecasting short-term electricity prices. Almost until the end of last century, electricity supply was considered a public service and any price forecasting which was undertaken tended to be over the longer term, concerning future fuel prices and technical improvements. Nowadays, short-term forecasts have become increasingly important since the rise of the competitive electricity markets. In this new competitive framework, short-term price forecasting is required by producers and consumers to derive their bidding strategies to the electricity market . Accurate forecasting tools are essential for producers to maximize their profits, avowing profit losses over the misjudgment of future price movements, and for consumers to maximize their utilities. Short-term load forecasting plays an important role in electric power system operation and planning . An accurate load forecasting not only reduces the generation cost in a power system, but also provides a good principle of effective operation. It is trained using back propagation algorithm and tested. The results obtained from neural network are presented and the results show that the neural network based approach is more accurate.