Feature extraction from spectrum to aid categorisation i have two sets of signals whose spectrum looks like shown in the figure. The first column of signals belongs to category 1 and the second belongs to category 2.
Looking at the spectrums I want to identify features which will differentiate the two.
Eg:mean,variance etc.
First thing to mention is that you must have some kind of Machine Learning algorithm to perform your recognition. You might want to use the following - what's yours?


Neural Networks (easy to implement)
SVM's (should perform best)
k-NN (eventually, but needs lot's of data to store)


Regarding features, it's hard to determine which to use for your application just by looking at spectra - both of categories have peaks at lower and higher part of range. Also there might be some significant information in time domain or a specific method valid for processing this type of signals. You never said what these signals are and what's the application. 

The only thing I notice is that class 2 has some harmonic content occurring at frequencies $160 \mathtt{Hz}$, and $200 - 225 \mathtt{Hz}$. I don't think that simple mean and variance will do. Probably you should try different and choose best performing ones. My suggestions of features you've asked for (to start with), are:


Spectral Slope (what's the gradient of the linear regression of your spectrum)
Audio Spectrum Flatness (calculated in frequency banks - tells you if signal is noisy or harmonic)
Audio Spectrum Centroid (kind of 'mean value' you mentioned)
Audio Spectrum Envelope (can be understood as very general spectrum descriptor - envelope)
MFCC (well defined and described - they are describing your spectrum by using cepstral analysis)


For more info about implementations you can refer to this great book (easy to get): MPEG-7 Audio and Beyond.