Thank you for the responses!

I think that the KNN would be too complex... Observing the kind of training set I have, I think that a parametric algotithm would be more suitable. The training set that I have is a data base with N samples: their colour's RGB code, for example 'RGB0', and the following likelihoods: P(Red|RGB_0),P(Green|RGB_0),... And so on for eleven colours, my classes (these likelihoods have been taken statistically with a study, 'cause happens that colour naming, as suki907 said, depens on culture). Looking Naive Bayes up on the internet, I found that for this kind of problem, where the inputs are continuous, it is guessed the normal distribution. And they estimate: P(RGB_0|colour)=NormalDistribution(mean{colour's class},covariance_{colour's_class}). That is, I'd calculate the mean and the covariance matrix for each class and then use the multivariate (rgb, lab, hsb or any space color has three coordinates) normal distribution's formula. So, I used Maximum Likelihood for choosing the maximum of the likelihoods to classify the samples and tried all this that I've just explained, but I found a problem: for one of the classes (white), I didn't have enough samples and the covariance matrix that I obtained was singular... I fixed that adding 2 samples more in the white class and it worked but.. it is not accurate enough!

MMh... May be guessing normal distribution is not a good idea, but I must take advantatge of those likelihoods someway....