In order to achieve effective targeting, Media Researchers require access to detailed data for every type of medium. Unfortunately, existing surveys tend to be too specific. For instance, television studies deal only with broad target groups and few surveys exist that deal with more than one type of medium. The ideal situation would be for media researchers to have access to single-source data providing detailed purchasing information combined with the usage of all types of medium. In practice, this is rarely achievable, due to cost, contractual arrangements and so on. The traditional solution to this problem has been the use of so-called fusion techniques, however the success of these techniques is marginal and their practical use of doubt, due to a number of well documented problems. Over the past few years, Pulse Train has been experimenting with Neural Networks - a general method of training a computer based on the structure of the brain - to learn about the relationships between questions in a detailed survey which then allows answers to these questions to be imputed within other surveys. The technique, which we call NDA (Neural Data Ascription), has met with some considerable success, certainly providing significantly improved results over current fusion methods.