Localizing micro cracks in critical components is crucial in the field of continuous structural health monitoring. In this paper, we utilize several signal processing and machine learning techniques such as hierarchical clustering and support vector machines (SVM) to process multisensor acoustic emission (AE) data generated by the inception and propagation of cracks. We present preliminary laboratory results that explore the pairwise event correlation of AE waveforms generated in the process of controlled crack propagation, and use these characteristics for clustering AE. By averaging the AE events within each cluster obtained from hierarchical clustering, we compute super-acoustics with higher signal to noise ratio (SNR) and use them in the second step of our analysis for calculating the time of arrival information (TOA) for crack localization. We utilize a SVM classifier to recognize the so called P-waves in the presence of noise by using features extracted from the frequency domain for accurate earliest arrival detection. Preliminary results show that our method has the potential to be component of a structural health monitoring system based on acoustic emissions for instance for bridges.