Sign language develops independently from the spoken language of the region . The sign language used in India is commonly known as Indian Sign Language (ISL). A functioning sign language recognition system can provide an opportunity for a deaf/mute person to communicate with non-signing people without the need for an interpreter. Our system deals with images of bare hands, which allows the user to interact with the system in a natural way. In doing so, we have designed a collection of ANFIS networks, each of which is trained to recognize one sign gesture. Features of the input gesture of the sign are extracted obtaining feature vector. The recognition algorithm translates each quantitative value of the feature into fuzzy sets of linguistic terms using membership functions. The membership functions are formed by the fuzzy partitioning of the feature space into fuzzy equivalence classes, using the feature cluster centers generated by the subtractive clustering technique. The subtractive clustering algorithm and the least-squares estimator are used to identify the fuzzy inference system, and the training is achieved using the hybrid learning algorithm.