Neural networks have been untrendy since the late 90s. This has decreased research since then. The general opinion of practical people now is that nnets with a single hidden layer are one of the major workhorses in the classification/regression toolbox. Neural nets with many hidden layers (needed to really advance the state of the art) are still a work in progress. There has been a lot of work on "deep" networks, usually focusing on unsupervised training, and then either sticking an support vector machine on the end, or finally training a single supervised layer. Practical people don't really use that stuff yet.