Software development is very slow process, expensive process & error prone usually resulting in the products with a huge number of problems which cause serious and major mistakes in usability, reliability, and performance. To overcome this problem, software measurement provides a systematically and empirical-guided approach to control and Improve software development process. However, due to high cost linked with "metrics collection" and the difficulties in "metrics decision-making," measurement is not universally adopted by software organizations. However Software Project Telemetry is still one of the finest Solutions to this problem. The Conventional approach in software project telemetry is to use few automatic sensors to collect all metrics & further decision making is done based on them. But the main problem comes when it becomes very difficult to classify the collected metrics data & that's why if we need trained intelligent sensor based software project telemetry, it is ideal to use artificial neural network.