||This thesis is focused on multi-sensor event detection schemes in Wireless Sensor Network (WSN). In the WSN, sensors individually take their observations, do some initial local processing and then send the result to the fusion center. Apart from this information, the fusion center may have prior information such as the known positions of sensors, the topology of the network or structures, features and patterns present in the received observations. In order to achieve better detection performance, the fusion center must exploit all this prior information in the best possible way. Keeping this in mind, in this thesis we focus on the exploitation of available a-priori information with the aim of developing enhanced and robust detection mechanisms. For instance, we exploit the fact that in most applications, the signal emitted from the event becomes a local physical phenomenon that only a"ects a small subset of sensors. Moreover, the a"ected sensors will be located close to the event as well as close to each other in the form of a spatial cluster. Hence, novel detection schemes are presented with a two-fold motivation: !rst, the exploitation of the relevant set of sensors, which helps in rejecting the noise; second, to take advantage of the signal correlation by using a-priori information about the positions of sensors. Based on these results, we move one step further and concentrate on the exploitation of both spatial and temporal correlation in the received observations. In this case, we propose detection schemes that explore di"erent matrix structures embedded in the observed covariance matrix, which naturally arise due to the underlying topologies of the multiple sensors. Numerical results are presented for all of the proposed schemes that show important advantages compared to traditional schemes.