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This paper presents a new automatic system for on-line change detection in structural health monitoring. The system is based on a combination of a Hopfield neural network with an adaptive kernel density estimation method and a test for unimodality. The changes in the process being monitored are detected from a model of that process. A Hopfield neural network is used on-line to adapt the model, tracking parameter variations from the process data. Given that these data are often corrupted by random noise, the parameter estimator implemented by the network can be regarded as a random vector. In this context, an adaptive kernel density estimation method is used to estimate the marginal probability density functions of the parameter estimator. When a parameter changes, the corresponding estimator marginal density becomes nonunimodal and this change is automatically detected by a test for unimodality. The robustness of the proposed system is guaranteed by the robustness of both the network and density estimation method. The system performance in structural health monitoring is illustrated by means of a simulation study, where a comparison is carried out with another approach to the problem of on-line change detection.