This paper presents a scheme of principal node analysis (PNA) with the aim to improve the representativeness of the learned codebook so as to enhance the classification rate of scene image. Original images are normalized into gray ones and the scale-invariant feature transform (SIFT) descriptors are extracted from each image in the preprocessing stage. Then, the PNA-based scheme is applied to the SIFT descriptors with iteration and selection algorithms. The principal nodes of each image are selected through spatial analysis of the SIFT descriptors with Manhattan distance (L1 norm) and Euclidean distance (L2 norm) in order to increase the representativeness of the codebook. With the purpose of evaluating the performance of our scheme, the feature vector of the image is calculated by two baseline methods after the codebook is constructed. The L1-PNA- and L2-PNA-based baseline methods are tested and compared with different scales of codebooks over three public scene image databases. The experimental results show the effectiveness of the proposed scheme of PNA with a higher categorization rate.