CAPTCHA is a type of challenge-response test used to determine human users from computer ones by generating challenges that are easy to solve for humans but difficult to solve for computers. One of the most common CAPTCHAs today is text-based where a short word is placed in a jumbled image. This project intends to break simple text-based CAPTCHAs automatically by implementing image preprocessing, filtering, and image segmentation followed by back propagation algorithm of neural network for recognition. Out of 320 CAPTCHAs, 311 CAPTCHAs were successfully decoded which means 97.187% accuracy was achieved with a simple multilayer perceptron network. This result could suggest that simple test based CAPTCHA is easily breakable so CAPTCHAs that are more difficult to segment and decode must be used instead.