Break-junction experiments are used to statistically study the electronic properties of individual molecules. The measurements consist of repeatedly breaking and merging a gold wire while measuring the conductance as a function of displacement. When a molecule is captured, a plateau is observed in the conductance traces otherwise exponentially decaying tunnel traces are measured. Clustering methods are widely used to separate these traces and identify potential sub-populations in the data corresponding to different molecular junction configurations. As these configurations are typically a priori unknown, unsupervised methods are most suitable for the classification. However, most of the unsupervised methods used for the classification perform poorly in the identification of these small sub-populations of molecular traces. Robust removal of tunnelling-only traces before clustering is thus of great interest. Neural networks have been proven to be powerful in the classification of data samples with predictable behaviour, but often show large sensitivity to the underlying training data. In this study we report on a neural network method for the separation of tunnelling-only traces in conductance vs. displacement measurements that achieves excellent classification performance for complete and unseen data sets. This method is particularly useful for data sets in which the yield of molecular traces is low or which comprise of a significant number of traces displaying a jump from tunneling features to a molecular plateau.