In this paper, the change vector analysis for hyperspectral imagery is investigated. Although plentiful change information is included in the change vectors with very high dimensionality, which permits the potential of finer change analysis, it is also very challenging to analyze these change vectors since any simple deterministic approaches using vector magnitudes and directions may not be feasible. In addition, the prior knowledge of ground truth is unavailable in most practical cases, where change analysis has to be completely unsupervised. Aiming at these difficulties, a simple but efficient relative radiometric normalization method is analyzed, and two automated approaches for change detection and classification using the hyperspectral change vectors after normalization are introduced. The experiment using real CASI datasets demonstrates the promising result.