Speech is the easiest and the most basic mode of communication in our day-to-day life. Hence, speech impaired people are at a major disadvantage in this concern. The staple mode of communication for the speech impaired is Sign Language which in turn isolates them from the rest of the world in more ways than one. The Sign Language Recognition (SLR) System is an attempt to compress this isolation by recognizing the gestures of the Sign Language. The application may be used to map the sign into their corresponding text or speech in the given spoken language. In general, the system has been realized either using image processing techniques or with sensor signals employed in smart gloves. The system based on image processing captures the hand gestures made by an individual and maps it to their corresponding alphabets, words or phrases pre-defined in the existing dataset. Although this system proves to be useful, the dwindling accuracy of the image capturing process is a major drawback. The sensor-based system, on the other hand, works using a number of sensors attached to a glove which detects the hand gestures and computes it to its corresponding meaning. The performance of these systems is relatively higher but they also have a few setbacks, one of which is its high price due to the involvement of sensors making it unattainable to people of all economic strata. In addition to this, the extensive usage of wires and other electronic devices increases the weight of the glove making it inconvenient to be worn for long hours. There are a number of Sign languages existing from various parts of the world, each of which have their own recognition systems individually. This comes out as another drawback of the Sign Language Recognition Systems as they aren’t adaptable to all Sign Languages and are restricted to a specific Sign Language as per its dataset. This paper illustrates the state of art of SLR with the detailed descriptions on various Sign Languages, dataset and recognition methodology and the mode of output, accuracy and application.