We explore the blending of model-based and deep learning approaches for target recognition in inverse synthetic aperture radar (ISAR) imagery. It evaluates five different approaches, namely, a model-based geometric hashing approach, a supervised deep learning approach, and three different blending models that fuse the model-based and deep learning approaches. The model-based approach extracts scattering centers as features and requires domain experts to identify and characterize important features of a target, which makes the training process very costly and hard to generalize when the image quality degrades in low signal-to-interference-plus-noise-ratio conditions. Next, a deep learning algorithm using a convolutional neural network is considered to extract the spatial features when raw ISAR data are used as input. This approach does not need an expert and only requires the labels of images for training. Finally, three model-based and deep learning approaches are blended together at the feature level and decision level to benefit from the advantages of both approaches, achieving a higher performance. The results show that the blending of the two approaches achieves a high performance while providing explainable inferences. The performance of the five different approaches is evaluated under varying conditions of occlusion, clutter, masking of the target, and adversarial attacks. It is empirically shown that the model-based and deep learning approaches are able to complement each other and can achieve better classification accuracy upon fusing the integrated approach.