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The performance assessment of a WDM network considering physical impairments is a difficult task and is frequently accomplished by using time consuming computational simulations. On the other hand, we observed that several metrics have been proposed to assess different aspects of a network structure. In this paper we propose that a set of metrics can be combined in order to obtain a fast estimation of a WDM network performance, based on a historical database of networks. The estimator was obtained by means of the most used Artificial Neural Network (ANN) architecture, called Multi-Layer Perceptron, that was trained using the classical back-propagation algorithm. According to our results, it is possible to build an estimator based on network metrics that assess WDM networks considering the trade-off between the processing time and the precision of the results. Our study also suggests that this kind of estimator can be easily adapted to other scenarios of WDM networks since Artificial Neural Networks present interesting characteristics, such as adaptation and flexibility.