This paper presents a new methodology for classifying underlying causes of voltage sag disturbances using the space phasor model (SPM) with normalized cross-correlation (NCC) techniques. Voltage sags are commonly occurring power quality disturbances in power system due to various underlying reasons like, transformer energization (TE), induction motor starting (IMS), system faults (LLLG), and load fluctuations. The correct identification of root cause is crucial for the effective mitigation strategies. The proposed methodology depicts the SPM technology to represent voltage sag waveforms in a multidimensional space, capturing both magnitude and phase information. The NCC technique is used to identify the similarity between template and reference sag waveforms pattern associated with different underlying causes of voltage sags. The NCC gives the green color rectangular window of given underlying cause. The combination of SPM and NCC gives a robust classification framework of underlying cause identification of voltage sags. The advantage of this approach over the other classification approach is that, this approach does not require any training and testing datasets. The proposed methodology is tested over the simulation datasets of different underlying reasons of voltage sags. The classification accuracy of this methodology is 100% without any ambiguity.