Following the notable success of artificial neural network-based approaches and their extensive applications in the biological domain, explainable AI has become a vibrant area of research. This surge in interest stems from the necessity to understand the internal working of artificial neural networks and to identify the most essential features for the classification tasks. This is a crucial precondition to get insight into the underlying biological structure of data and to ground any clinical translation. Over the past decade, numerous machine learning methods, primarily heuristic-based, have been developed to tackle the problem of explainability. Due to the uncertainty of the proposed explanations, explainability remains an open area of research. Recently, approaches based on symbolic AI have been introduced. Due to the inherent interpretability and explicit representation of knowledge and reasoning processes, they allow for exhaustively enumerating explanations and identifying minimal ones. In this work, we adapt one such approach and use answer set programming (ASP) for computing explanations. We develop a logic program to formulate artificial neural networks and adapt a deletion-based algorithm to identify which combinations of features in the input data are most influential in determining a network’s output. We present the results of our approach on six different benchmark scenarios: heart disease diagnosis, thyroid recurrence, breast cancer, diabetes, E. coli promoter, and voting. Furthermore, we compare our results with machine learning-based algorithm as well as existing logic-based techniques. Our results indicate that the ASP-based approach is competitive with other logic-based techniques and in some instances even generates smaller explanations, further emphasizing its potential in practical applications and highlighting the potential of combing neural networks with ASP to improve interpretability.