Sign language is used as a primary form of communication by many people who are deaf, deafened and non-verbal. Communication barriers exist for members of these populations during daily interactions with those who are unable to understand or use sign language. Advancements in technology and machine learning techniques have enabled development of innovative approaches to translate these sign languages to spoken languages. This paper proposes an intelligent system for translating sign language into text. This approach consists of hardware as well as software. The hardware consists of flex, contact, inertial sensors and SD card module mounted on a synthetic glove, additionally If-Else (Rule based learning) based learning is performed by Arduino nano (Atmega328p) to represent the proposed system. This system is able to recognize static letters, numbers and translating 26 letters from A to Z and 10 numbers from 0 to 9 from the American sign language. The database with the use of alphabet and numbers is prepared, and tested by kNN and CN2 rule inducer, where kNN has shown promising result. This experiment is done by Orange software. Experimental results demonstrate that our system is effective, cheaper and has high classification accuracy as compared to other technology available in market.