The human wrist pulse signal, which often exhibits nonstationarity, can reflect changes in mechanisms and pathophysiology in the blood and viscera. In this paper, we present an integrated approach for identifying hypertension using the wavelet packet transform (WPT) and continuous hidden Markov models (HMM) to analyze wrist pulse signals. The approach starts with decomposing the wrist pulse signals into a number of frequency sub-bands through the WPT. Then the local discriminant bases (LDB) algorithm is used to obtain the best representation of the wrist pulse signal in the optimal frequency sub-bands. After that, energy features are extracted from those sub-bands and the optimal features associated with corresponding sub-bands are selected using the Fisher linear discriminant criterion. The optimal features are subsequently used as input to a continuous HMM classifier for hypertension identification. Experimental results indicate that the presented approach can differentiate the hypertensive wrist pulses from healthy wrist pulses effectively. In addition, when compared with other classifiers, the results demonstrate that the continuous HMM classifier yields a better classification result.