Most techniques for explainable machine learning focus on a single modality for the explanations, e.g., using either feature or example attribution. A novel approach, called Hybrid Attribution Network (HAN), is proposed for providing multimodal explanations for image classification. The technique first extracts embeddings from a deep neural network (DNN), which are subsequently used by a KNN classifier to form predictions; example attributions can then be derived from the latter. Based on the example attributions, pixel attributions are further generated to provide complementary feature-level explanations. Results from an empirical investigation show that HAN may provide highly concentrated example attributions, i.e., the predictions can be explained with few training examples, without compromising predictive performance relative to the original deep neural network. Moreover, the pixel attributions are shown to enhance the interpretability of the predictions, by highlighting key pixels in the example attributions. An important finding from the empirical investigation is that the choice of layer to use for the embeddings may have a large impact on both the predictive performance and the generated explanations.