We consider the 3D object recognition problem from the perspective of the lack of labelled data. In this paper, we propose a novel progressive conditional generative adversarial network (PC-GAN) for 3D object recognition by conditioning the input with progressive learning strategies. PC-GAN is a powerful adversarial model whose generator automatically produces realistic 3D objects with annotations, and the discriminator distinguishes them from the training distribution and recognizes their categories. We train the discriminative classifier simultaneously with the generator to predict the class label by embedding a SoftMax classifier. Progressive learning uses input samples from lower to higher resolutions to increase the generator performance gradually and produce informative objects for a certain class of objects. The key idea of adopting progressing learning is to mitigate overshoots issues of the discriminator and increase variations in the generated objects by learning progressively. This strategy helps the generator to produce more realistic synthetic objects and improve the active classification performance of the discriminator. Our proposed PC-GAN is trained for object classification in a supervised manner and the performance is evaluated on two public datasets. Experimental results demonstrate that our adversarial PC-GAN outperforms the existing volumetric discriminative classifiers in term of classification accuracy.