Affective phenomena, as reflected through brain activity, could constitute an effective index for the detection of music preference. In this vein, this paper focuses on the discrimination between subjects electroencephalogram (EEG) responses to self-assessed liked or disliked music, acquired during an experimental procedure, by evaluating different feature extraction approaches and classifiers to this end. Feature extraction is based on time-frequency (TF) analysis by implementing three TF techniques, i.e., spectrogram, Zhao-Atlas-Marks distribution and Hilbert-Huang spectrum (HHS). Feature estimation also accounts for physiological parameters that relate to EEG frequency bands, reference states, time intervals, and hemispheric asymmetries. Classification is performed by employing four classifiers, i.e., support vector machines, k-nearest neighbors (k-NN), quadratic and Mahalanobis distance-based discriminant analyses. According to the experimental results across nine subjects, best classification accuracy {86.52 (±0.76) %} was achieved using k-NN and HHS-based feature vectors (FVs) representing a bilateral average activity, referred to a resting period, in beta (13-30 Hz) and gamma (30-49 Hz) bands. Activity in these bands may point to a connection between music preference and emotional arousal phenomena. Furthermore, HHS-based FVs were found to be robust against noise corruption. The outcomes of this work provide early evidence and pave the way for the development of a generalized brain computer interface for music preference recognition.