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“Performance breakdown” of maximum-likelihood (ML) direction-of-arrival (DoA) estimation is analyzed. “Performance breakdown” occurs when signal-to-noise ratio (SNR) and/or training sample volume fall below some threshold values and a ML set of DoA estimates calculated for properly detected number of sources, unavoidably contains an estimation “outlier”. In this paper, we propose a technique to “predict” (i.e. identify, recognize) the underlying scenario and an ML set of DoA estimates, as potentially containing an outlier and specify these potential outliers.