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Classifying wood species accurately is crucial since incorrect labelling of wood species may incur huge loss to timber industries. An automated wood species recognition system is designed based on image analysis of the wood texture which consists of image acquisition, feature extraction, and classification. There are 100 images captured from each wood sample which are divided into training samples and testing samples. An effective feature extractor is important to extract most discriminant features from the wood texture in order to distinguish the wood species accurately. Therefore, in this paper, a novel feature extractor based on Improved-Basic Gray Level Aura Matrix (I-BGLAM) technique is proposed to extract 136 features from each wood image. Fundamentally, the proposed I-BGLAM feature extractor which focuses on the gray level of the wood images is rotational invariant and has smaller feature dimension …