Musical instrument recognition is one of the important goals of musical signal indexing. If much effort has already been dedicated to the automatic recognition of musical instruments, most studies were based on limited amounts of data which often included only isolated notes. In this paper, two statistical approaches, namely the Gaussian Mixture Model (GMM) and the Support Vector Machines (SVM), are studied for the recognition of woodwind instruments using a large database of isolated notes and solo excerpts extracted from many different sources. Furthermore, it is shown that the use of Principal Component Analysis (PCA) to transform the feature data significantly increases the recognition accuracy. The recognition rates obtained range from 52.0 % for Bb Clarinet up to 96.0 % for Oboe.