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Li-ion Batteries are one of the most critical components of the next generation Hybrid Electric Vehicles (HEV) as degradation or failure of the Li-ion battery could lead to reduced performance, operational impairment and even catastrophic safety issues. An effective diagnostics and prognostics system for Li-ion battery health monitoring would greatly improve the reliability of such systems and thus secure general public acceptance. This paper presents a similarity-based health assessment method for Li-ion battery. Instead of physically diagnosing the health of the Li-ion battery, the proposed method defines the healthy operations (charging and discharging) as the baseline and the deviation from this baseline is treated as the degradation. Specifically, novel features are extracted from the voltage, current and temperature measurements firstly. Then Principal Component Analysis (PCA) is applied to minimize the dimensionality of the multivariate feature space.