The application of feature extraction methodologies and the detection of patterns in sagitae otoliths, which are calcified structures in the inner ear of teleostean fishes, has lead to great knowledge of marine biology during the last decades in order to manage and control its sustainability. A main limitation of the use of statistical analysis and Fourier methods rely on their incapacity to locate irregularities and explain them from a more structural, or even physical, point of view. This matter has been addressed recently by means of the Best-Basis paradigm which combines robust description methods, such as the Wavelet Transform, and the potential of statistical analysis in order to fully automate the selection process of efficient features. This paper is an attempt to readdress this paradigm towards this goal and contrasts other standard tools used in the field of otolith-based fish recognition. The proposed strategy involves the estimation of class distributions, discriminant measures and the search in the feature space.