diff --git a/DataStructures/Heaps/MinPriorityQueue.java b/DataStructures/Heaps/MinPriorityQueue.java
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+
+/* Minimum Priority Queue
+* It is a part of heap data structure
+* A heap is a specific tree based data structure 
+* in which all the nodes of tree are in a specific order. 
+* that is the children are arranged in some
+* respect of their parents, can either be greater
+* or less than the parent. This makes it a min priority queue
+* or max priority queue.
+*/
+
+// Functions: insert, delete, peek, isEmpty, print, heapSort, sink
+
+public class MinPriorityQueue {
+    private int[] heap;
+    private int capacity;
+    private int size;
+
+	// calss the constructor and initializes the capacity
+    MinPriorityQueue(int c) {
+        this.capacity = c;
+        this.size = 0;
+        this.heap = new int[c + 1];
+    }
+
+	// inserts the key at the end and rearranges it
+	// so that the binary heap is in appropriate order
+    public void insert(int key) {
+        if (this.isFull())
+            return;
+        this.heap[this.size + 1] = key;
+        int k = this.size + 1;
+        while (k > 1) {
+            if (this.heap[k] < this.heap[k / 2]) {
+                int temp = this.heap[k];
+                this.heap[k] = this.heap[k / 2];
+                this.heap[k / 2] = temp;
+            }
+            k = k / 2;
+        }
+        this.size++;
+    }
+
+	// returns the highest priority value
+    public int peek() {
+        return this.heap[1];
+    }
+
+	// returns boolean value whether the heap is empty or not
+    public boolean isEmpty() {
+        if (0 == this.size)
+            return true;
+        return false;
+    }
+
+	// returns boolean value whether the heap is full or not
+    public boolean isFull() {
+        if (this.size == this.capacity)
+            return true;
+        return false;
+    }
+
+	// prints the heap
+    public void print() {
+        for (int i = 1; i <= this.capacity; i++)
+            System.out.print(this.heap[i] + " ");
+        System.out.println();
+    }
+
+	// heap sorting can be done by performing
+	// delete function to the number of times of the size of the heap
+	// it returns reverse sort because it is a min priority queue
+    public void heapSort() {
+        for (int i = 1; i < this.capacity; i++)
+            this.delete();
+    }
+
+	// this function reorders the heap after every delete function
+    private void sink() {
+        int k = 1;
+        while (2 * k <= this.size || 2 * k + 1 <= this.size) {
+            int minIndex;
+            if (this.heap[2 * k] >= this.heap[k]) {
+                if (2 * k + 1 <= this.size && this.heap[2 * k + 1] >= this.heap[k]) {
+                    break;
+                } else if (2 * k + 1 > this.size) {
+                    break;
+                }
+            }
+            if (2 * k + 1 > this.size) {
+                minIndex = this.heap[2 * k] < this.heap[k] ? 2 * k : k;
+            } else {
+                if (this.heap[k] > this.heap[2 * k] || this.heap[k] > this.heap[2 * k + 1]) {
+                    minIndex = this.heap[2 * k] < this.heap[2 * k + 1] ? 2 * k : 2 * k + 1;
+                } else {
+                    minIndex = k;
+                }
+            }
+            int temp = this.heap[k];
+            this.heap[k] = this.heap[minIndex];
+            this.heap[minIndex] = temp;
+            k = minIndex;
+        }
+    }
+
+	// deletes the highest priority value from the heap
+    public int delete() {
+        int min = this.heap[1];
+        this.heap[1] = this.heap[this.size];
+        this.heap[this.size] = min;
+        this.size--;
+        this.sink();
+        return min;
+    }
+
+    public static void main(String[] args) {
+        // testing
+        MinPriorityQueue q = new MinPriorityQueue(8);
+        q.insert(5);
+        q.insert(2);
+        q.insert(4);
+        q.insert(1);
+        q.insert(7);
+        q.insert(6);
+        q.insert(3);
+        q.insert(8);
+        q.print(); // [ 1, 2, 3, 5, 7, 6, 4, 8 ]
+        q.heapSort();
+        q.print(); // [ 8, 7, 6, 5, 4, 3, 2, 1 ]
+    }
+}
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