Although ultrasound imaging has been widely applied in medical diagnosis for decades, the data processing remains primitive. Traditional B-mode ultrasound imaging exhibits the amplitude of the scattered ultrasonic signals as brightness of the images, neglecting rich information delivered by the frequency modulation of the signals. The frequency variation in the spectrum contains nonlinear vibrations which are specific for given tissues. We hypothesize that detailed analysis and characterization of the spectrum enable the software to recognize the signals from different organs or from diseased regions. Wavelet transform was utilized to exhibit the ultrasonic signal in both time and frequency domain, followed by the principal component analysis which extracted the feature of the frequency. This analysis holds the potential of intelligent diagnosis by ultrasound imaging.