The current COVID-19 pandemic has motivated the researchers to use artificial intelligence techniques for potential alternatives to reverse transcription polymerase chain reaction (RT-PCR) due to the limited scale of testing. The chest X-ray (CXR) is one of the alternatives to achieve fast diagnosis but the unavailability of large scale annotated data makes the clinical implementation of machine learning-based COVID detection methods difficult. Another important issue is the usage of ImageNet pre-trained networks which does not guarantee to extract reliable feature representations. In this paper, we propose the use of hierarchical convolutional network (HCN) architecture to naturally augment the data along with diversified features. The HCN uses the first convolution layer from COVIDNet followed by the convolutional layers from well known pre-trained networks to extract the features. The use of the convolution layer from COVIDNet ensures the extraction of representations relevant to the CXR modality. We also propose the use of ECOC for encoding multiclass problems to binary classification for improving the recognition performance. Experimental results show that HCN architecture is capable of achieving better results in comparison to the existing studies. The proposed method can accurately triage potential COVID-19 patients through CXR images for sharing the testing load and increasing the testing capacity.