This paper summarizes various algorithms used to design a sign language recognition system. Sign language is the language used by deaf people to communicate among themselves and with normal people. The authors designed a real time sign language recognition system that can recognize gestures of sign language from videos under complex backgrounds. Segmenting and tracking of non-rigid hands and head of the signer in sign language videos is achieved by using active contour models. Active contour energy minimization is done using signers hand and head skin color, texture, boundary and shape information. Classification of signs is done by an artificial neural network using error back propagation algorithm.