This paper presents the analysis of phase current signals to identify and quantify common faults from an electrical motor based on dynamic time warping (DTW) algorithm. In condition monitoring, measurements are often taken when the motor undertakes varying loads and speeds. The signals acquired in these conditions show similar profiles but have phase shifts, which do not line up in the time-axis for adequate comparison to discriminate the small changes in machine health conditions. In this study, DTW algorithms are exploited to align the signals to an ideal current signal constructed based on average operating conditions. In this way, comparisons between the signals can be made directly in the time domain to obtain residual signals. These residual signals are then based on to extract features for detecting and diagnosing the faults of the motor and components operating under different loads and speeds. This study provides a novel approach to the analysis of electrical current signal for diagnosis of motor faults. Experimental data sets of electrical motor current signals have been studied using DTW algorithms. Results show that DTW based residual signals highlights more the modulations due to the compressor process. And hence can obtain better fault detection and diagnosis results.