In engineering, Structural Health Monitoring (SHM) is an important field of study representing a fundamental process to control the longevity and reliability of structures during service. The objective of an SHM is to detect and quantify the structure degradation at an earlier stage. The acquisition of such information can contribute to prevention of total failure and hence avoiding human and financial losses becomes more possible. With the growing demands for cost-efficient and robust products, SHM is facing particularly high requirements. This thesis focuses on the development, implementation, and experimental validation of an innovative SHM system able to detect, identify, and classify in an extensive way damage mechanisms occurring in different materials. Several techniques can be applied for in situ health monitoring. In this work, Acoustic Emission Technique (AET) is used. Acoustic Emission is a passive nondestructive evaluation technique referring to the elastic waves generated by energy release during microstructural changes in the material. Those changes arise as a result of mechanical and environmental stresses. Monitoring of such a conversion can be continuously done in real-time using suitable hardware and advanced signal processing methods. The performance and reliability of an AE-based damage diagnosis approach are highly dependent on material, structure design and the damage scenarios. Therefore, a Field Programmable Gate Array (FPGA)-based measurement chains developed for sensing and acquiring the generated AE signals. This chain is easily adaptable to different structures and materials. It was therefore kept so far constant as possible throughout all tests conducted. Additionally to the use of highly efficient hardware that enhance the sensing quality and the data acquisition speed, the implementation of advanced filtering techniques with high processing accuracy is of central importance. The main objective of this thesis is to prove the function of the system developed to analyze AE waves under different damage scenarios. For this purpose, three different materials namely wear resistant plates, laminated composite plates, and electrochemical cells are investigated. Owing to the diversity of the studied materials, special attention is paid to the development and implementation of multilevel signal processing approach and pattern recognition methods. The processing chains are capable to detect, quantify and qualify the AE data, whereby AE-based characteristics are identified and correlated with the corresponding AE sources. The designed diagnosis methodology concentrates/focuses on damage detection (feature extraction), damage estimation (feature selection), and damage classification by using time-frequency analysis, multilevel statistical approaches, and supervised classification methods. The results obtained show a noticeable/remarkable enhancement of the identification and classification of damage mechanisms. The efficiency of applying multilevel processing approach is/(could be) thus proved. The methodology presented here, allows an automated structural health monitoring. Hereby, an important step forward in future development of safe and reliable structures is represented.