Broad Learning System (BLS) offers an alternative way of machine learning in deep structure. BLS is established based on the idea of the random vector function-link neural network (RVFLNN) which eliminates the drawback of long training process and also provides the generalization capability in function approximation. In this paper, a principal polynomial features based broad learning system (PPFBLS) is proposed. In this method, the principal component analysis (PCA) is used for feature dimensionality reduction. The candidate features of degree d are constructed by the principal features of degree one and the principal features of degree d-1. The enhancement features of degree d is extracted by applying PCA on the candidate features of degree d. Ridge regression learning using the concatenated features of each degree are applied for pattern classification. Parameters in the feature extraction stage are optimized by PCA which is different with randomly initialization adopted by BLS and RVFLNN. Experimental results on the MNIST handwritten digits recognition data set and the NYU NORB object recognition data set demonstrate the effectiveness of the proposed method.