This paper presents a noval framework that classifies finger movements automatically using Wavelet Transform and its derivatives by capturing statistical features from the discrete time Electromyogram (EMG) signals. In the suggested method, wavelet-based denoising is used to separate out the subject's EMG signals, and then Discrete Wavelet Transform (DWT) and Wavelet Packet Transform are used to decompose the signals and extract their key characteristics (WPT). The derivatives of the feature sets are employed to analyse the correlation among them. This method is motivated by the surveillance that there exists a distinctive correlation between the different features of the samples of the signals extracted at various frequency levels. Experimentally, it was perceived that this correlation varies from signal to signal. Both Feed forward and Cascaded Feed forward Artificial Neural Networks (ANN) are used for classification. Experiments show that the proposed method significantly improves the classification rate. The performance of the suggested wavelet-based features and their derivatives in combination with ANN and trained with the Levenberg-Marquardt algorithm was evaluated by comparing the simulation results for various sets of features. Comparing the new method benefits to earlier traditional methods in terms of classification performance helped to further highlight their advantages. These experimental findings demonstrate that the suggested approach performs admirably in classifying finger movements based on EMG signal patterns. The suggested methodology also helps clinicians increase the reliability of myoelectric pattern recognition.