To add to Thierry's answer, you can think of the error as a function of the weight vector i.e. as a function from $R^n \rightarrow R$ which you would like to minimize. The back propagation algorithm works by looking at a local neighborhood of a point and seeing which direction will lead to a smaller error. This will then give you a local minimum.
What you want it a global minimum, but you have no guaranteed way of finding it. And if your surface has several local minima then you may be in trouble.
But if it has only a few then Thierry's strategy should work - performing multiple searches for local minima by starting at randomly selected points should increase the chances of your finding the global minimum.
And in the happy case in which there is only one minimum - any initial weight vector will lead you to it.