Hybrid intelligent systems use several Soft Computing techniques to help solve complex problems. Pattern analysis is one such domain which extensively uses hybrid intelligent systems for efficient understanding of complex problems. Extracting patterns is the principal step in object recognition and in the past years many different pattern extraction techniques have been introduced. To further enhance its efficiency, hybrid intelligent techniques have been used which have proven very useful. Handwritten digit recognition is a challenging problem faced by various researchers. It is an important problem in optical character recognition and it has been used as a test case for theories of pattern recognition and machine learning algorithms for many years. Machine recognition of handwritten digits has many practical applications, as in reading handwritten notes in a PDA, postal addresses on envelopes, amounts in bank checks, and handwritten fields in forms. This chapter provides a complete understanding of how pattern recognition is done in a real life case study with respect to recognizing handwritten digits. All traditional techniques used in recognizing handwritten digits and its significance are studied and reviewed. Further, we explain the utility of hybrid techniques and how they have proved to be more powerful than independent existing ones. The chapter also provides an in-depth methodology involved in tackling pattern recognition problems and provides elaborate details regarding each technique ranging from data acquisition and feature extraction to classification and recognition. © 2018 by Taylor & Francis Group, LLC.