This paper proposes a novel wood species recognition scheme based on the spectral reflection features of wood surface, aiming to address the following three issues in terms of the noise filtering, feature selection and radian’s optimal design . First, noises occur in some bands of wood spectral reflection curve so that these noisy bands should be deleted. Second, the wood spectral band is 350~2 500 nm, which is a 2 150D vector with a spectral sampling interval of 1 nm. Therefore, both noise filtering and feature selection should be performed to wood spectral data. In this paper, to simultaneously and efficiently solve the two problems of feature selection and noise filtering, both a feature selection procedure and a noise filtering procedure are performed by solving the eigenvalues of dispersion matrix. This scheme is novel and produces a good outcome. Third, to make the spectral reflection curves picked up by the spectral instrument have the best pattern recognition information; an optimal design is performed for the indoor radian's mounting height. The genetic algorithm is used to solve the optimal radian's height so that the spectral reflection curves have the best classification information for wood species. Therefore, the optimal design scheme for the radian's mounting height can improve the pattern classification accuracy of the wood species to some extents, which is novel with excellent executive feasibility. Many experiments made with our developed software system on the five ordinary wood species in northeast region of China (i.e., including Betula platyphylla, Populus davidiana, Pinus Sylvestris, Picea jezoensis, Larix gmelinii) are performed for approximately 105 times. It indicates that the overall recognition rate reaches to a good recognition accuracy of 95% for five wood species with an ideal recognition velocity. The selected feature wavelengths by using of our feature selection algorithm based on dispersion matrix are mainly in the near infrared band.