do autoencoders work well for non images? I have a classification problem for which a feedforward, fully connected neural net works reasonably well (two classes, true positive and true negative rate close to 80%).

I want to get these rates to 90%, and more features is one of the catalysts for improvements I can think of.

Do autoencoders to learn additional, interesting features work well for problems that do not involve images?
Yes, but no-one can tell if they will work well for your problem, so just try it and see. Don't give up if it does not work at first, because training neural networks requires some practice; there are lots of parameters, and not every configuration will work well. Even the optimization algorithm is a hyperparameter.