In this paper we propose the integration of Data Mining with Hidden Markov Models when applied to the problem of acoustic bird species recognition. We first show how each of them is applied on an individual manner, contrast their results and devise a model to combine them for targeted classifications. Previous work has shown that large collec- tions of spectral attributes are needed in order to represent the structure of bird songs, therefore elevating the compu- tational requirements when applied to distributed sensor networks. Data Mining is used to reduce the dimension- ality of the spectral attributes and for classification. Hid- den Markov models represent a traditional approach and require strong song preprocessing. Our results show that Data Mining can yield efficient results with low require- ments and could serve to target HMMs input parameters.