This paper proposes feature extraction methods for object classification with passive acoustic sensor networks deployed in (sub-)urban environments. We analyzed the emitted acoustic signals of three object classes: guns (muzzle blast), vehicles (running piston engine) and pedestrians (several footsteps). Based on the conducted analysis, methods are developed to extract features that are related to the physical nature of the objects. In addition, localization methods are developed (e.g. pseudo-matched-filter), because the object location is required for one of the feature extraction methods. As a result, we developed a proof-of-concept system to record and extract discriminative acoustic features. The performance of the features and the final classification are assessed with real measured data of the three object classes within sub-urban environment.