Fiber optic damage sensors are transforming the landscape of structural health monitoring through real-time, highly accurate detection of strain, cracks, and pressure variations. In this paper, we compare algorithms based on multivariate data analysis as well as data processing using neural networks, comparing their performance on a real structure. Introduction Fiber Bragg Gratings (FBGs) began to be used as strain sensors in the early 1990s, and approximately a decade. Fiber-optic sensors cannot measure damage; to get information about damage from strain measurements, additional strategies are needed, and several alternatives are available in the existing literature. This paper discusses two independent procedures. Their high sensitivity and immunity to electromagnetic interference make them ideal for use in diverse environments.