This study introduces a novel method for detecting and sorting spikes from multiunit recordings, combining wavelet transform and superparamagnetic clustering (SPC). The wavelet transform localizes distinctive spike features, while SPC allows automatic classification without assumptions like low variance or Gaussian distributions. An improved amplitude thresholding method is proposed for spike detection. The algorithm is designed to be unsupervised and fast, and its performance is compared with conventional methods using simulated data sets that closely resemble in vivo recordings. The results show that the proposed algorithm outperforms conventional methods in terms of accuracy and efficiency. The method is particularly useful for classifying spikes from a large number of channels recorded simultaneously, and it demonstrates robustness to various conditions such as overlapping spikes, bursting activity, and electrode movement.This study introduces a novel method for detecting and sorting spikes from multiunit recordings, combining wavelet transform and superparamagnetic clustering (SPC). The wavelet transform localizes distinctive spike features, while SPC allows automatic classification without assumptions like low variance or Gaussian distributions. An improved amplitude thresholding method is proposed for spike detection. The algorithm is designed to be unsupervised and fast, and its performance is compared with conventional methods using simulated data sets that closely resemble in vivo recordings. The results show that the proposed algorithm outperforms conventional methods in terms of accuracy and efficiency. The method is particularly useful for classifying spikes from a large number of channels recorded simultaneously, and it demonstrates robustness to various conditions such as overlapping spikes, bursting activity, and electrode movement.