Abstract: In this study, we explore Machine Learning (ML) techniques to indoor Wireless Local Area Network (WLAN) Fingerprints (FPs) parameterisation and classification in academic environments. First, relevant indoor location (received signal strength indication and site specific) features were abstracted from the proposed area of study (University of Uyo, Nigeria) in a previous research to serve as fingerprints to the current research. Second, an unsupervised principal component analysis methodology was employed to produce Principal Component Dominant Features (PCDFs) for the first three principal components (components with eigenvalues of at least unity). These components revealed the degree of variances exhibited by the selected FPs. Third, using three ML classifiers (Support Vector Machine: SVM, k-Nearest Neighbour: k-NN, decision tree and Adaptive Neuro-Fuzzy Inference System: ANFIS) a classification of the PCDFs was performed. Results obtained showed that decision tree and linear SVM classifiers were excellent at predicting large datasets an important precursor to accommodating scalability in WLAN environments and areas with localisation challenges such as difficult terrains, heavy interference and spatial or uneven distribution of wireless infrastructure as these classifiers maintained high classification accuracies of above 90%. For small datasets, ANFIS gave good classification accuracy when compared with other classifiers.