In this study, the Market Clearing Price (MCP) is forecasted with Artificial Neural Networks and the modeling success is examined for different preprocessing strategies. The purpose of the study is to obtain the optimum model with a significant estimation success and to provide the best price prediction. The hour-based electricity generation data of diverse production items are assigned as inputs and the resulting MCP is modeled. The raw data are first cleaned from outliers, then subjected to different normalization processes and 70 different ANNs are trained. Additionally, networks are trained with data classified in seasons and the effect of seasonal patterns on model success is observed. Finally, networks showing the optimum performance are selected. It is noted that the type of the normalization strategy and the hidden layer size are the key factors to make a decent estimation. Then, in order to test the networks with extreme cases, data for the special days (official holidays) are applied to these networks as input. The success of the networks is evaluated by comparing the MCP predictions with the actual values. It is revealed to make a prediction for official holidays, a model which is special to this period of year is required.