Sign Language (SL) is a parallel communication medium to spoken language, mostly used by people who are not able to speak or hear. A human interpreter is also required to work as a mediator or to act like a bridge to reduce communication gap between deaf/dumb people and normal people. However a well-trained human interpreter is not easily available and is also not cost-effective. Besides, it also compromises confidentiality and accuracy. To overcome these problems Sign Language Recognition System (SLRS) has been developed as an automatic and intelligent mediator for the communication. For a decade different Machine Learning (ML) techniques have been being used to design SLRS. This paper highlights about SL, gives a general framework of SLRS and reports different ML approaches proposed for the design of SLRS. This paper also reports Deep Learning (DL) models proposed for SLRS and finally highlights the challenges encountered in the design of SLRS.