Equalization : using the spectral or the temporal signal? I'm trying to equalize a sound signal - using a JAVA program - and I'm using this process : 
1/ Conversion of the temporal signal to a spectral signal, using a FFT

2/ Applying a coefficient to each frame of the spectral signal to equalize it

3/ Conversion of the spectral signal modified to a temporal signal

4/ Reading of that temporal signal

If I'm applying that process to a "pure" signal (ex : a 400 Hz sinusoïdal signal generated "on the fly" by a java class), it seems to work. But if I'm applying it to a "real" wav signal (a song, for example), the result is unsusable. I hear a kind of "sliced" sound, even if all the equalization coefficients are equal to "1" (=no modification).

So, to equalize a sound, does the process I describe above is the right solution ?
Or shall I avoid that double conversion and applying a convolution product on the temporal signal ?

If I have to do apply a convolution product, how to do it ? I have no clue about calculating it with "random" signals.

Thank you for all your answers.
If you are trying to make a proper filter, you would want to use FFT convolution, which is like OLA, but not the same.  OLA is more of a synthesis technique for reducing noise between frames caused by incoherent phase mangling from directly manipulating frequency data.
You also need to resolve the phase of the bins for your filter.  You are only defining the magnitudes and phase at those discrete frequencies, not the frequencies in between, which can be radically different then what you have in your head.  The easiest thing to do is to use linear phase to center the impulse of the filter in the window.  This can be done by multiplying each bin magnitude by e^jpik.
If you are trying to realize an analog prototype, this is a bad way to do it.  Your frequency bins are linearly spaced, but analog filters are logarithmic.  The effect will be that your lower bands have a lower “Q” and vice versa.  You can still do it, but it may not do what you wanted.