The research on predicting the spectral reflectance information of objects based on the camera RGB value has always attracted researchers’ attention. The traditional method is to restore spectral reflectance through the information under a single light source. Recently, Zhang et al. [Color Research & Application, 2017, 42: 68] proposed a two-step method for predicting reflectance based on camera RGB information under a single light source. Firstly, the camera response RGB value under a single light source is transformed to CIE XYZ values under different light sources through a polynomial model with local training samples using a pseudo-inverse method. Then the reflectance can be estimated based on the predicted CIE XYZ values under multiple light sources using local training samples and pseudo-inverse method. Though the method still uses camera RGB information under a single light source, obtaining CIE XYZ values under multiple illuminants improves the reconstruction accuracy of the spectral reflectance. Motivated by Zhang et al., a new two-step method is proposed for reconstructing spectral reflectance based on the raw camera RGB. Firstly, camera raw RGB is transformed to CIE tristimulus values under multi-illuminants via polynomial expansion of order 3 and weighted least square approach. Reflectance of the object is predicted based on the transformed CIE tristimulus values under multi-illuminants using the Wiener estimation. The prosed method uses the full set of training samples in order to avoid selecting a certain number of training samples that existed with the method of Zhang et al. Hence the proposed method is easy to be applied. Furthermore, in the method of Zhang et al., selected training samples are considered as equalimportant, while the proposed method assigns different weights to each of the training samples depending on the closeness to the given test sample in the first step so that prediction accuracy is increased. Comparison between the proposed method and the method given by Zhang et al. is considered. Both methods are trained using the X-Rite Color Checker Standard Digital (SG) chart and tested using the Color Checker Classic Chart and self-made 44 printed samples. Comparison results have shown that the proposed method outperforms the method given by Zhang et al. in terms of root mean square error (RMSE) and CIEDE2000 colour difference. Furthermore, the prediction accuracy of the proposed method is improved with the increase of the number of illuminants used, and the proposed method performs the best with 6 illuminants.