The sensor response of a camera can be represented as the stimulus multiplied by the spectral distribution of an ambient illuminant, the surface reflectance of an object, and camera sensitivity. Surface reflectance is one of the most significant factors that indicates an object's color; therefore its estimation has received widespread attention. Among conventional methods for estimating surface reflectance, principal component analysis (PCA) has an advantage because it uses only one set of principal components for an entire reflectance population. There are limitations, however, in estimating all reflectance using this PCA method with only one set of principal components. In this article, an algorithm is proposed to estimate surface reflectance by using principal components determined by subgroups with similar colors, which are classified from the entire reflectance population. In order to compose a subgroup with similar colors, the Macbeth ColorChecker is utilized to obtain initial representative surface reflectance values for an entire reflectance population; then the Munsell chips are divided into subgroups with different principal components. Moreover, initial representatives have to be modified to avoid biased representations for the population because the Macbeth ColorChecker does not provide optimal representations for the entire reflectance population, even though it is evenly spaced in the CIELAB color space. Therefore, the mean value of each subgroup is used to obtain new representatives, and the new subgroups of reflectance are composed by using the Lloyd quantizer design algorithm. Then, the PCA method is applied for the principal components of the subgroup including surface reflectance. To evaluate its performance, the proposed estimation method was compared with that of a conventional three-band principle component analysis. The proposed method provided better results in its performance.