The last few decades have seen a significant development of complex heat transfer enhancement geometries such as a helicallyinned tube. The arising problem is that as the fins become more complex, so does the prediction of their performance. In addition to discussing existing prediction tools, this dissertation demonstrates the successful use of artificial neural networks as a correlating method for experimentally- measured heat transfer and friction data of helicallyinned tubes.