understanding PCA results and SVD

Hello, I have been recently reading a lot and trying to understand the princomp() and svd() functions from matlab. So I read the threads and recommended papers, but it seems I am not able to find the answer to my problem. I have the concept, but cannot get it numerically. So, here it goes: Let's say I have m signals (6), which are varying with time, which is discrete. So, I have n samples (20) of this m signals. This signals are somehow linear combinations of some primary signals, which is what I want to find. Therefore, for example, let's say I ant to find the 2 signals that leinarly combined will give me this 6 signals. So, I costruct the matrix A(n,m). Here comes the first problem , according to matlab or depending if is svd or pca I should introduce A(n,m) or A'(m,n). The more I read the more confused I get about how should I introduce them. Then I delete the mean value of each signal and perfomr [U S V]=svd(A,0) and [coeff,score,latent] = princomp(X); From here I get several signals, but, for what I read, from SVD, V should be the principal components, but for me, the principal components are 2 signals of 20 samples each...and what I get is a 6x6 matrix... For PCA is more or less the same. So, putting it more clear, what I am not able to retieve is something that will make me possible to do: A=loads*pc; I want to know loads and pc from svd of pca. Also know which method is better. And, final question is, if one of my origianl signals does have a mean value, whay sould I delete it? If I delete it I will never be able to recompute the original signals... I know is quite long, but I hope I was able to express myself. Thanks in advance for your help