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B-ISDN is expected to support a variety of services, each with their own traffic characteristics and quality of service requirements.Such diversity however, has created new congestion control problems, some of which could be alleviated by a traffic prediction scheme. This paper investigates the applicability of artificial neural networks for traffic prediction in broadband networks. Recent work has indicated that such prediction is indeed possible, as the neural networks are able to learn a complex mapping between past and future arrivals. Such work however has been based on the use of artificially generated traffic, and by definition the past and future arrivals are related. In this paper we consider real traffic and show that prediction is possible for certain traffic types but not for others. We also demonstrate that simple linear regression prediction techniques perform equally as well as neural networks.