Demand for power quality analysis in industrial networks has increased in recent years. Voltage sags are the most frequent and impactful disturbances, with especially high financial implications for industrial clients. Understanding the underlying causes behind voltage sags is a first step towards a mitigation solution. In this paper, we propose a voltage sag cause identification algorithm based on instantaneous symmetrical components and dynamic time warping applied to voltage and current measurements. Short-Time Fourier Transform and Fortescue transform are implemented in the pre-processing stage, obtaining a four-dimension time series signature. Then, a simple but effective multivariate time series classification approach is used. It is based on 1-Nearest Neighbor classifier and dependent Dynamic Time Warping as distance measure (1NN-DTW D ). The main advantages of the proposed method are the electrical interpretability of the signatures and the reduced size of the training data. A classification accuracy of 100% is reached with synthetic data, representing seven voltage sag sources. The method reaches a classification accuracy ratio higher than 98% with a reduced real dataset representing five event classes.