Influenza viruses continue to evolve rapidly and are responsible for seasonal epidemics and occasional, but catastrophic, pandemics. We recently demonstrated the use of decision tree and support vector machine methods in classifying pandemic swine flu viral strains with high accuracy. Here, we applied the technique of artificial neural networks for the prediction of important influenza virus antigenic types (H1, H3, and H5) and hosts (Human, Avian, and Swine), which fulfills a critical need for a computational system for influenza surveillance. A comprehensive experiment on different k-mers and different binary encoding types showed classification based upon frequencies of k-mer nucleotide strings performed better than transformed binary data of nucleotides. It has been found for the first time that the accuracy of virus classification varies from host to host and from gene segment to gene segment. In particular, compared to avian and swine viruses, human influenza viruses can be classified with high accuracy, which indicates influenza virus strains might have become well adapted to their human host and hence less variation occurs in human viruses. In addition, the accuracy of host classification varies from genome segment to segment, achieving the highest values when using the HA and NA segments for human host classification. This research, along with our previous studies, shows machine learning techniques play an indispensable role in virus classification.