In this paper, a data-driven stochastic subspace identi?cation (SSI-DATA) technique is proposed as an advanced stochastic system identi?cation (SSI) to extract the inter-area oscillation modes of a power system from wide-area measurements. For accurate and robust extraction of the modes’ parameters (frequency, damping and mode shape), SSI has already been veri?ed as an effective identi?cation algorithm for output-only modal analysis. The new feature of the proposed SSI-DATA applied to inter-area oscillation modal identi?cation lies in its ability to select the eigenvalue automatically. The effectiveness of the proposed scheme has been fully studied and veri?ed, ?rst using transient stability data generated from the IEEE 16-generator 5-area test system, and then using recorded data from an actual event using a Chinese wide-area measurement system (WAMS) in 2004. The results from the simulated and recorded measurements have validated the reliability and applicability of the SSI-DATA technique in power system low frequency oscillation analysis.