In this work, musical instrument recognition is considered on solo music from real world performance. A large sound database is used that consists of musical phrases ex-cerpted from commercial recordings with different instrument instances, different players, and varying recording conditions. The proposed recognition scheme exploits class pairwise feature selection based on inertia ratio maximization. Moreover , new signal processing features based on octave band energy measures are introduced that prove to be useful. Classification is performed using Gaussian Mixture Models in a one vs one fashion in association with a data rescal-ing procedure as pre-processing. Experimental results show that substantial improvement in recognition success is thus achieved.