ugh wrote virtually an entire essay then accidentally deleted it.. So here a shorter and possibly less flippant version:
1 using lvq, or fuzzyfying the inputs can improve generelisation. but depending on your application is probably not necessary
2. it may cause suppression of the underrepresented classification targets. is 9 too much, that depends on your data set
3. in principle a single hidden layer is sufficient to approximate any function, given enough nodes. I've read that the funneling the data through 3 hidden layers with the middle one smaller than the outer ones (e.g 5 3 5) can help with strongly non linear data. in my personal experience having more hidden layers resulted in faster convergence.
4.yes backwards propagation. it is not hard too implement, it is essentially a gradient ascent algorithm
5. cant hurt i think, and i can think of situations where not doing so could hurt :). provided you know that your domain is bounded.
neural nets converge to a local optimum, so changing the starting conditions can influence the end result. this may or may not be a problem depending really on your particular situation. one approach which has worked well is to construct several networks to do the same thing and to simply average the result. generally speaking though 'playing around' is the best way forward
