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This paper presents a novel approach for determining post-contingency dynamic vulnerability regions (DVRs), oriented to assess vulnerability in real time as part of Smart Grid applications. Based on the probabilistic models of input parameters, such as load variation and the occurrence of contingencies, Monte Carlo-type simulation is performed to iteratively evaluate the system time domain responses. The dynamic probabilistic attributes are then analyzed using time series data mining techniques, namely Multichannel Singular Spectrum Analysis (MSSA), and Principal Component Analysis (PCA), in order to recognize the system DVRs based on the patterns associated to three different short-term stability phenomena. The vulnerability criterion consists in the possibility of some N-1 contingencies driving the system to further undesirable events (i.e. N-2 contingencies), which could be considered as the beginning of a cascading event. The proposal is tested on the IEEE New England 39-bus test system. Results show the feasibility of the methodology in finding hidden patterns in dynamic electric signals as well as in numerically mapping power system DVRs due to its ability to consider relevant operating statistics, including the most probably severe events that could lead the system to potential insecure conditions and subsequent blackouts.