Does a multilayer perceptron taught by simple backprop learn the shape of a character or the exact image of a character? (Feel free to suggest a better title) (same with tags, none of relevant tags exist yet, and I only have 101)

Let's say I have a perceptron with an optimal number of layers and optimal number of neurons, which I have taught to recognize an alphabet which was printed in a certain font, with the same settings and resolution. Now if I feed it a symbol from that set, but say, printed in another (but similar) font, or with the same font, but rotated 5 degrees, or shifted 2 pixels to the left, or scaled a 5 percent, and so on, will this system recognize this modified symbol, or at least have an easier time learning to recognize this symbol? (than a system without any prior learning)

Basically, what is the point of back-propagating neural networks? Is it
1) a way to recognize shapes, and effectively filter noise in images of those shapes, in form of different rotations, scales and other disortions
or
2) just a relatively simple and fast way to recognize a limited number of bitmaps of those shapes, and not being able to recognize the shape in another bitmap if that particular bitmap was never taught to the network.
I would be very surprised if a backpropagation-trained multilayer FFN spontaneously learned concepts of shape. It's not that the notion of "shape" isn't representable by a FFN (given enough layers, anything is). It may even be the case that the globally optimal configuration does capture something about shape. But there must be a strong local minimum about the exact-image replicator, meaning that typical backpropagation (i.e., gradient descent) algorithms will get stuch in such local minima. Unless you've specifically primed your FFN to learn shape, I would be very surprised if it did.