In distribution systems, High Impedance Faults (HIFs) occur due to downed live conductors connected to a high impedance ground surface or unbroken conductor touching leaning tree. This contact restricts the current value between 0 and 75 A depending on the high impedance surface [1]. The conventional relays are incapable of detecting currents with these low magnitudes. In this context, fallen live conductors pose a threat to humans and their property. HIFs in distribution systems often generate fires and cause loss of power supply to consumers. Therefore, HIF detection has now become a big issue for power utilities. In this work, a 11 kV system is chosen because a huge number of HIFs come with supply voltages of 15 kV or less, with the difficulty faced in HIF detection being worse at lower voltages. The difficulty is less severe above 15 kV, but HIFs can occur at these voltages as well [1]. The HIF phenomena and detection methods are comprehensively documented in [2]. The method proposed in [3] measures and analyzes the harmonic content to understand what extent this is useful in HIF detection. Another method based on the concept of fractal geometry analyzes chaotic properties of fault current [4]. The primary problem with these methods is setting a threshold, which affects the performance of the detection method. A reliable detection scheme using time-frequency analysis for feature extraction is in [5,6]. Time-frequency analysis is highly sensitive to a non-stationary signal and shows good performance in all the detection criteria. However, the time consumed during the computations raises a question about the utilization of this method in protection applications. The Wavelet Transform (WT) method is capable of processing the signal by analyzing low- and high-frequency components. A method based on WT detects HIF using wavelet decomposed coefficient of a voltage signal [7,8]. Both voltage and current signal features are extracted using wavelet packet transform for detection and discrimination of HIF [9]. The standard deviation of detail and approximation coefficient of level 3 [10] and level 7 [11] of db4 mother wavelet is used for the detection process. In addition to WT feature extraction, Principal Component Analysis (PCA), Bayes classifier [12,13] and fuzzy inference system [14] are used for classification into HIF and non-HIF. WT provides a better resolution in signal processing. However, high computational burden, high sampling rate and memory space requirements restrict the use of this method in protection applications. In [15], the amplitude and phase information of HIF current signal is proposed. Mathematical Morphology (MM) [16,17] was recently employed in HIF detection and the method needs an enhancement.