It is still challenging to reconstruct the spectral reflectance of a surface using digital cameras under given luminance and observation conditions. A new approach to solving the problem which is based on neural network and basis vectors is proposed. At first, the spectral reflectance of the sample surface is measured by spectrometer and the response of an digital camera is recorded. Then the reflectance is represented as a linear combination of several basis vectors by singular value decomposition (SVD). After that, a neural network is trained so that it is able to approximate the relationship between the camera responses and the coefficients of basis vectors accurately. In the end, the spectral reflectance can be reconstructed based on the neural network and basis vectors.In the present paper, the authors reconstructed the spectrum reflectance based on neural network and basis vectors. Compared with other traditional methods, neural network expands the space of unknown function F(S) from linear functions to more general nonlinear functions, which gives more accurate estimation of the coefficients ak and better reflectance reconstruction. Results show that the reflectance of standard Munsell color patch (Matte)can be reconstructed successfully with mean of RMS which is 0.023 4. Compared with linear approximation method, reconstruction of standard Munsell color patch (Matte)using this approach reduces the reconstruction error by 67%. Since the neural network can be implemented by Matlab neural network toolbox, this method can be easily adopted in many other cases. Therefore we conclude that this approach has advantages of higher accuracy, easy implementation and adaptation, thus can be used in many applications.