Borderline personality disorder (BPD) is a serious mental illness that can cause significant suffering and carries a risk of suicide. Assigning an accurate diagnosis is critical to guide treatment. Currently, the diagnosis of BPD is made exclusively through the use of clinical assessment; no objective test is available to assist with its diagnosis. Thus, it is highly desirable to explore quantitative biomarkers to better characterize this illness. In this study, we extract spectral power features from the power spectral density and cross spectral density of resting-state fMRI data, covering 20 brain regions and 5 frequency bands. Machine learning approaches are employed to select the most discriminating features to identify BPD. Following a leave-one-out cross validation procedure, the proposed approach achieves 93.55% accuracy (100% specificity and 90.48% sensitivity) in classifying 21 BPD patients from 10 healthy controls based on the top ranked features. The most discriminating features are selected from the 0.1∼0.15Hz frequency band, and are located at the left medial orbitofrontal cortex, the left thalamus, and the right rostral anterior cingulate cortex. The high classification accuracy indicates the discriminating power of the spectral power features in BPD identification. The proposed machine learning approach may be used as an objective test to assist clinical diagnosis of BPD.