The problem of PCA is that it is very different of the metric you put on your feature space. Divide/multiply per three the coordinate of one feature can move from one important axis to a small one.

In most case where PCA is used for ML algorithm, it is a very important thing to take into account since you can lose a feature which is quite discriminating but that you'll squeeze in a discarded axis if its coordinate is too small.

There are obviously methods to avoid this like whitening the input data but it doesn't cut it completely.