Countries aiming for sustainability in economic growth and development ensurethe reliability of energy supplies. For countries to provide their energy needsuninterruptedly, it is important for domestic and renewable energy sources to beutilised. For this reason, the supply of reliable and sustainable energy has becomean important issue that concerns and occupies mankind. Of the renewable energysources, wind energy is a clean, reliable and inexhaustible source of energy withlow operating costs. Turkey is a rich nation in terms of wind energy potential.Forecasting of investment efficiency is an important issue before and during theinvestment period in wind energy investment process because of high investmentcosts. It is aimed to forecast the wind energy products monthly with multilayerneural network approach in this study. For this aim a feed forward backpropagation neural network model has been established. As a set of data, windspeed values 48 months (January 2012-December 2015) have been used. Thetraining data set occurs from 36 monthly wind speed values (January 2012-December 2014) and the test data set occurs from other values (January-December2015). Analysis findings show that the trained Artificial Neural Networks (ANNs)have the ability of accurate prediction for the samples that are not used at trainingphase. The prediction errors for the wind energy plantation values are rangedbetween 0.00494-0.015035. Also the overall mean prediction error for thisprediction is calculated as 0.004818 (0.48%). In general, we can say that ANNs beable to estimate the aspect of wind energy plant productions.