Machine learning is a powerful paradigm to analyze Proton Magnetic Resonance Spectroscopy (1H-MRS) spectral data for the classification of brain tumor pathologies. An important characteristic of this task is the high dimensionality of the involved data sets. In this work we apply filter feature selection methods on three types of 1H-MRS spectral data: long echo time, short echo time and an ad hoc combination of both. The experimental findings show that feature selection permits to drastically reduce the dimension, offering at the same time very attractive solutions both in terms of prediction accuracy and the ability to interpret the involved spectral frequencies. A linear dimensionality reduction technique that preserves the class discrimination capabilities is additionally used for visualization of the selected frequencies.