News article classification is a recently growing area of interest in text classification because of its associated multiple matching categories. However, the weak reliability indices and ambiguities associated with state-of-the-art classifiers often employed make success in this domain very limited. Also, the high sensitivity and large disparity in performance results of classifiers to the varying nature of real-world datasets make the need for comparative evaluation inevitable. In this paper, the accuracy and computational time efficiency of the Kolmogorov Complexity Distance Measure (KCDM) and Artificial Neural Network (ANN) were experimentally evaluated for a prototype large dimensional news article classification problem. 2000 News articles from a dataset of 2225 British Broadcasting Corporation (BBC) news documents (including examples from sport, politics, entertainment, education and technology, and business) were used for categorical testing purposes. Porter’s algorithm was used for word stemming after tokenization and stop-words removal, and a Normalized Term Frequency–Inverse Document Frequency (NTF-IDF) technique was adopted for feature extraction. Experimental results revealed that ANN performs better in terms of accuracy while the KCDM produced better results than ANN in terms of computational time efficiency.