Structural Health Monitoring (SHM) systems of civil engineering structures aim to assess and ensure the safety of structures and people. This paper describes a real-world application of an SHM system in an industrial steel tower structure. This is a high steel structure, containing several mechanical equipment with dif-ferent loads, which operate at various frequencies. The monitoring approach in-cludes a computer modeling of the structure, primarily used to define the sensor network. The sensors were mounted at predetermined locations designed to con-tinuously measure vibrations and deformations at critical points. The sensor net-work consists of an array of sensors and a gateway. The sensors include strain gauges and triaxial accelerometers, as well as other weather sensors. Data are ac-quired both from time series of values observed at regular intervals and from structurally relevant measured values, called events, where specific data are col-lected. Machine learning is used in the development of statistical models for fea-ture discrimination. A visualization user interface is provided to access all data through a user friendly and accessible tool. The paper presents the main results obtained so far, with the primary assessment of the structural health conditions.