In the palmprint identification system, feature fusion has been emerging an effective way to improve system performance. Feature vectors extracted from palmprint and fusion strategy are key factors in this procedure. This paper discusses issues about the two aspects respectively. (1) The performance will decline considerably as the number of categories increase. This is determined by the intrinsic characteristics of the selected feature vectors. To overcome this problem, we propose a feature selection method based on K-L distance between feature vectors of each person and the population in each dimension. Experimental results show that the performance of the system keeps a stable level when the categories increase. (2) Whether the fusion of different feature vectors can bring improved performance, it depends on which feature vectors should be chosen. For feature level fusion (FLF), feature vectors should have weak correlation between each other. In this paper, we studied the correlation based on Spearman correlations coefficient. If the matrix reflects a strong correlation, this fusion strategy should be a failure. We test two fusion strategies in experiment. A) Feature vectors extracted by principle components analysis method (PCA) plus feature vectors extracted by PCA method from images after DCT transform. B) Feature vectors extracted by PCA method plus feature vectors extracted by independent components analysis (ICA) method. Experimental results show that strategy B brings an improved performance, for the features vectors in this strategy with weak correlation.