In this paper we introduce a novel feature extraction method based on Nonnegative Matrix Factorization (NMF) for hyperspectral image processing. Given the large size of the hyperspectral imagery, feature extraction plays an important role in producing fast and accurate results. Traditional approaches such as Principal Component Analysis and Independent Component Analysis generate the features as a linear combination of the hyperspectral bands emphasizing on the decorrelation or independence of the features. Compared to this, NMF offers a decomposition solution that is less restrictive requiring only the positivity of the features and the associated linear transform. Such scenario has a natural meaning in hyperspectral imagery where each pixel observation is thought to be formed as a linear positive mixture of reflectance values of the materials in the scene (endmembers) covered by the pixel. With hyperspectral imagery spatial resolution ranging from millimeters to kilometers, it likely that the data observed are formed as a mixture. In this case, the linear transform used to generate the features would be associated to the endmembers and the resulting features would be associated to the abundance of each endmember in the pixels. We present our results on using NMF for feature extraction by performing experiments with hyperspectral digital imagery collection experiment (HYDICE) data as well as in-house imagery collected with a SOC 700 hyperspectral camera. The experiments suggest that NMF outperforms PCA in feature and endmember extraction.