Non-intrusive Load Monitoring (NILM) offers elegant, cost-effective, scalable, and load-specific electricity consumption monitoring compared to the traditional way of equipping loads with sensors. NILM techniques have been studied extensively for residential loads. Industrial loads offer challenges for NILM, such as phase imbalance associated with 3-phase lines. Therefore, this work presents a load recognition technique for NILM applying low complexity Fortesque Transform (FT). The FT decomposes the unbalanced 3-phase current waveform extracted from 3-phase aggregate power measurements to balance the given load. The 3-phases current waveform is transformed into an image-like representation using a compressed-euclidean distance matrix to improve the recognition ability further. The image representation is used as input to Convolutional Neural Network (CNN) classifier to learn the patterns of labeled data. Experimental evaluation of the industrial aggregated dataset shows that FT improves recognition performance by 5.8\%, compared to the case without FT.