This thesis presents a comprehensive exploration of implementing common guitar effects in real time, using signal processing techniques with Python and some of its libraries. One key focus of the thesis is latency reduction using Cython. The thesis begins with an overview of digital signal processing (DSP) fundamentals and common effects for the electric guitar, such as distortion, delay and reverberation. Some effects’ algorithmic implementation is also discussed, highlighting the main components and parameters required for real-time processing. Subsequently, the thesis introduces Python as a powerful tool for prototyping and implementing DSP algorithms. Utilising libraries such as NumPy, Sounddevice and Librosa, the feasibility of real-time guitar effects processing within the Python environment is demonstrated. Moreover, the flexibility of Python, facilitating rapid experimentation and algorithm refinement crucial for achieving desired sound characteristics, is also highlighted. To address the challenge of latency inherent in software-based signal processing, the benefits of Cython, a superset of Python designed to optimise code performance, are explored. Through Cython's capability of compiling Python code to native machine code, significant latency reductions are achieved without compromising computational efficiency. Experimental results demonstrate the effectiveness of the proposed approach in achieving low-latency digital guitar effects processing in real time. Comparative latency measurements reveal improvements over traditional Python implementations, highlighting the potential adequacy as well as importance of Cython optimisation for latency-sensitive applications.