An interesting possibility for improved surveillance capabilities in challenging underwater environments and against targets with low acoustic signatures is the use of multisensor systems combined with data- or information fusion. This work describes how data fusion can be used for tracking and classifying targets using passive underwater acoustic and electric field sensors. Tracking of a synthetic underwater target has been performed on data from a passive acoustic uniform line array (ULA) and a long base-line electrode array for underwater electric field measurements. A Kalman filter was applied on bearing estimates from the acoustic data and estimates of the target position from the electrode array. The results show that the electrode sensors are useful, within the limits of their sensitivity, for adding range estimates to the acoustic bearing information. Underwater acoustic and electric field signatures have been used to classify surface ships according to their size and propulsion. Two feature extraction methods have been used, an AR (Auto Regressive) model and a non-linear DDE (Delay Differential Equation) model. A Bayesian minimum-error classifier has been implemented on the extracted features. It is shown that data fusion can improve the classification performance in some cases. However, improvement requires that each underlying classifer work properly. In other cases, the performance might be degraded.