In this study, a new method for exposure time correction for hyperspectral imaging is introduced. Initially, hardware setup was established. Then, a look-up table holds the minimum and maximum exposure times for each band was built. By using the developed image acquisition system, images having different exposure times for each hyperspectral band were acquired. After that, various features that can represent the exposure state were identified and a dataset was established. Prediction performance of the proposed method was cross validated by artificial neural network and outcomes were interpreted. It is observed that, by using the proposed method desired exposure quality can be determined with 99% accuracy.